AI data enrichment platform that turns documents, spreadsheets, and images into structured, validated records. LLM data extraction with multi-model cross-checking, semantic IDs against duplicates, and up to 40 languages per field.
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Last probed Nov 3, 2026 · entityenricher.ai
58tools discovered
For AI agents — MCPBundles Directory API
For MCP client apps (human connect):
MCP connect URL: https://entityenricher.ai/api/mcp/
Skill page: https://www.mcpbundles.com/skills/entityenricher-mcp-0f5bb6da87
Explore capabilities (58)
read 7
write 18
Showing 25 of 58
Showing 25 of 58 tools from the public preview. Connect the server to inspect the full live tool list.
List4
Delete3
Get3
Create2
Other capabilities13
Upload attachment
write
upload_attachment
Upload base64 file bytes as reusable source material; returns id and requires_capability. Pass the ID in attachment_ids to sample/schema generation, enrichment or benchmarks. Supported format handling depends on server MIME policy: extracted text or model-readable binary. Prefer auto model selection for attachment capabilities. Uploading does not itself run an LLM. A generated sample with attachments is source-only; see enricher://docs/documents for formats, multiple-file behavior and research w
Description may be truncated on the public preview.
Upload attachment — Upload base64 file bytes as reusable source material; returns id and requires_capability. Pass the ID in attachment_ids to sample/schema generation, enrichment or benchmarks. Supported format handling depends on server MIME policy: extracted text or model-readable binary. Prefer auto model selection for attachment capabilities. Uploading does not itself run an LLM. A generated sample with attachments is source-only; see enricher://docs/documents for formats, multiple-file behavior and research w
Delete attachment — Permanently delete an attachment in your organization, including its stored file. No LLM call. Existing records remain, but future calls or schema regeneration cannot reuse the deleted source. Delete only when that source is no longer needed; this is not a required post-enrichment step. Returns the deleted ID and filename.
Start batch enrichment — Start billed asynchronous enrichment of an entity list against exactly one of schema_id or target_schema. Returns job_id and total. No fixed entity-count cap; live prompt quotas and credits can stop remaining work. Each entity follows the enrichment/fusion pipeline; every model must succeed for its automatic fusion and database admission. A confident classification mismatch skips that entity without enrichment, never pauses the batch. Poll get_job_status, then list_records(job_id=...). Attachmen
List benchmark scenarios — List compact benchmark scenario summaries and total. Scenarios test enrichment, sample generation or schema generation. No LLM call. Read one with get_benchmark_scenario or create one with create_benchmark_scenario. Lifecycle and reference requirements: enricher://docs/model-benchmark.
Get benchmark scenario — Read one benchmark scenario with per-model quality, cost and speed results. include_reference=true adds the reference, the fixed entity_data and the schema-generation entity_samples. Config changes can make existing results stale. No LLM call. For a ranked subset use get_benchmark_scenario_results. Read after run_benchmark completes.
Get ranked benchmark scenario results
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A working endpoint is step one. Directory coverage is the coordinated launch across ChatGPT, Claude, Cursor, the MCP Registry, and community indexes.
AI data enrichment platform that turns documents, spreadsheets, and images into structured, validated records. LLM data extraction with multi-model cross-checking, semantic IDs against duplicates, and up to 40 languages per field.
How do I connect Entity Enricher to my AI agent?
Use the MCP endpoint listed on this page in your MCP client configuration. One-click install pills support Claude, Cursor, VS Code, and other hosts. Copy the remote MCP URL if your client needs a manual entry.
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— Filter, rank and limit a scenario's per-model benchmark results. No LLM call. overall blends quality, speed and cost using organization task weights and is null if a component is missing. Status tags are independent: success does not exclude stale or stale_score results. Missing sort metrics come last in either direction. See enricher://docs/model-benchmark for interpretation.
Create benchmark scenario — Create a reusable benchmark with a mandatory scoring judge. scenario_type='enrichment' needs schema_id and entity_data (the entity to enrich, as enrich_entity takes it — refused when it carries no value; never put it in description); 'sample_generation' needs sample_request; 'schema_generation' needs entity_samples (1..20 samples of one entity type). Enrichment and schema generation need a verified gold reference via set_benchmark_reference before running. Sample generation is rubric-scored and
Update benchmark scenario — Edit a benchmark's test definition or scoring configuration. Requires owner and a plan with benchmarks; no model run. Only supplied fields change, but sample_params and schema_gen_params replace their parameter objects wholesale. The judge may be replaced, not cleared; scenario_type is immutable. Changed test definitions make previous results stale. Re-run affected models to refresh their scores. See enricher://docs/model-benchmark.
Set benchmark reference — Save the gold reference for an enrichment or schema-generation benchmark. Only set reference_verified=true after checking its values against trusted evidence or obtaining human sign-off; a generated answer alone is not verification. Schema-generation references must be GeneratedJsonSchema objects. Sample-generation scenarios reject references because they are rubric-scored. Requires owner and a plan with benchmarks; no LLM call. A verified reference enables run_benchmark. See enricher://docs/mod
Revert benchmark reference updates — Undo automatic edits a scoring pass made to a scenario's reference. Every scoring pass folds what the scored models showed the reference should be (a candidate the judge found better, a rule the samples prove, a value the reference lacked) and writes it into the reference — an edit a pass already made is only replaced by stronger evidence (samples, a wrong verdict, more agreeing models), never by one more model's better verdict; the log is reference_meta.auto_applied on get_benchmark_scenario, e
Delete benchmark scenario — Delete a benchmark scenario and its stored results. Requires owner and a plan with benchmarks. No LLM call; inspect the scenario before deleting it.
Run benchmark — Start billed asynchronous execution and scoring of a benchmark. Requires owner, a benchmark-enabled plan and a judge; a verified reference is also required except for sample generation. Supply model_keys or providers; omitting both runs all active models with usable provider keys. Repetitions and judging increase cost. Returns job_id and total_models: poll get_job_status, then read get_benchmark_scenario_results. Re-running replaces each selected model's previous result. An organization's benchm
List database syncs — List a saved schema's database registrations, linked schemas, options and sync hosts. Returns pending and quarantined delta counts, projection_upgrade_pending and database links. No LLM call. Pending zero alone does not prove healthy delivery: check quarantine and migration blockers. Use list_entity_states for server-side rows and get_record for per-record delivery state. PostgreSQL, MySQL and SQLite delivery is performed by the user's sync client. See enricher://docs/database-sync.
List entity states — Browse a schema's current merged entity rows, not per-run records. Requires editor; no LLM call. Returns identities, revision, last_record_id and optionally payload, with limit/offset pagination. Rejected entities have no row; purge_entity_state may also remove delivered rows. Server-side state does not prove the external replica has applied its deltas. Use get_record and list_database_syncs for delivery and rejection diagnostics. See enricher://docs/database-sync.
Create database sync — Register a saved schema for relational synchronization to PostgreSQL, MySQL or SQLite. Requires owner and a sync-enabled plan. Starts a billed classification job when available; returns database ID, classification_job_id or classification_skipped, stamped_keys and registration_notices. The webhook signing secret stays outside the MCP response and can be managed in the web app. The schema is initially unpublished: wait for classification, review keys/options/notices, then publish_schema before an
Assign sync host — Assign or clear the host provisioning a database sync. Requires owner and a sync-enabled plan. host accepts a connected host ID/name; null unassigns it. Assignment may create the physical database and start synchronization automatically. Moving hosts revokes the old host's minted credential; an existing manual pairing is not evicted. Candidates come from create_database_sync or list_database_syncs. No LLM call. Managed and manual setup: enricher://docs/database-sync.
Classify database model — Start a billed analysis proposing database keys, SQL types, indexes and relationship ownership on a linked schema. Requires editor. Registration already starts the initial pass; use this after relevant edits. Incremental scope covers new properties or changed JSON types/multilingual flags; an empty scope does not rerun unchanged fields. Returns job_id: poll get_job_status and inspect get_schema's working copy. Correct proposals with property tools, then publish_schema to ship changes. Entity-lev
Delete database sync — Delete a database registration and its queued deltas, stopping its feed. Requires owner and a sync-enabled plan; no LLM call. External replica tables remain untouched. Entity state and schema database flags remain by default; delete_entity_state and clear_database_model additionally remove data/model settings from schemas left with no registration. Obtain approval for those irreversible teardown options. See enricher://docs/database-sync.
Get database setup instructions — Return non-secret install, browser-confirmed pairing and run instructions for an ee-database sync client. Requires owner and a sync-enabled plan; no LLM call and no credential is issued or exposed to the MCP client. Run pair_command on the intended replica host: the CLI opens verification_url, the user chooses the database and confirms, and the credential travels directly to the polling CLI. Its DSN stays on that host. Use manual pairing only when managed host provisioning is not already handlin
Fetch database deltas — Read the next ordered window of SQL deltas and canonical payloads for a database sync. claim=false is replayable; claim=true leases the window for 120 seconds. Apply a claimed window transactionally, including schema deltas, before ack_database_deltas. limit is also bounded by the registration's page_limit. snapshot_required pauses delivery until the replica reapplies its snapshot. No LLM call. Leasing, cursors, quarantine and snapshot handling: enricher://docs/database-sync.
Acknowledge database deltas — Acknowledge every delta through up_to_id after successful application, releasing its lease. Acknowledgement can permanently purge delivered deltas and entity state according to registration options; never acknowledge unapplied or failed work. Returns acknowledged and purged counts. No LLM call. Apply/ack workflow: enricher://docs/database-sync.
Inject records into the database sync — Validate and inject stored or supplied enrichment output into the entity layer and linked syncs. May incur semantic-resolution cost. record_id alone reuses its output; adding structured_output creates a new derived record. Without record_id, supply structured_output and saved_schema_id. Each item uses the current published contract and admission gate. Only enrichment/arbitration records qualify; base records whose fusion is in the same request are skipped. Inspect each outcome for rejected or pa
List models — List available model keys, nominal capabilities, languages, strategies, auto-selected defaults and organization profile_limits. Use when choosing explicit models or checking plan limits; ordinary calls may omit models or use auto without fetching this large catalogue. Auto also accounts for attachment capabilities. is_available means a usable provider key exists, not that provider quota or every combination of tools and media will work. Missing capability flags mean unsupported on this discovery
Enrich entity — Enrich one entity against exactly one of schema_id or target_schema, returning structured output, record_id, costs and any database outcome. Read get_schema's input_contract first (published version when linked); never invent preserve values. Models may be omitted for auto selection. Generation is billed. Two or more models fuse only when all succeed: check failed_models before reporting success. A failed leg prevents automatic fusion and database admission. classification_warning returns succes
Retry failed expertises — Retry only an existing record's failed expertise domains, then update its output and attempt the run's fusion/synchronization. Billed for retried work. Requires failed_expertises on that record (get_record); a surviving successful sibling is not retryable and returns no_failed_expertises. Supply its entity_input_data and saved_schema_id; model optionally substitutes the failed model. Returns job_id: poll get_job_status, then re-read get_record. When the failed leg left no record, use a one-model
Showing the first 25 tools — full list and schemas: Directory API.
How many tools does Entity Enricher provide?
MCPBundles probed 58 tools on the live server. The tool list on this page reflects what was discovered at the last refresh — connect your client to see the full set available to your session.
What authentication does Entity Enricher require?
Entity Enricher may require signing in to the provider before tools can run. Connect through MCPBundles or your MCP client and complete any provider login when prompted.
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