Tanod MCP server

mlpeek: extract named entities from text

extract_entities

Read-onlyIdempotent

Extract named entities like people, organizations, places, dates, and money from English text, returning labels, code-point offsets, and per-label counts. Optionally filter by entity type.

Instructions

mlpeek: Extract named entities: people, organisations, places, dates, money, ... For fields of your own schema use extract_json_with_schema. Returns named entities in English text with spaCy en_core_web_sm 3.8.0 (MIT): text, label (the 18 OntoNotes types: PERSON, ORG, GPE, LOC, DATE, MONEY, ...) and code-point offsets (end exclusive), plus counts per label; at most 2,000 entities (then entities_truncated). A statistical model: it can miss or mislabel entities. English only. Runs an open model pinned by hash on Tanod's own CPU (no LLM, no third-party API); an unavailable model is a 503 (not charged). Typically under 0.1 s for 2,000 characters, under 0.5 s at 20,000. Price: USD 0.001. Free: 5 mlpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/named-entity-recognition-zero-shot-classification-api.html

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	English text, at most 20,000 characters.
`labels`	No	Only these entity types (default: all 18 OntoNotes types).

Output Schema

TableJSON Schema

Name	Required	Description
`note`	No
`error`	No	Only on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
`model`	No
`chunks`	No
`counts`	No
`entities`	No
`chars_analyzed`	No
`entities_truncated`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 15, 2026

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes far beyond annotations: pinned model hash, CPU-only execution, no third-party API, 503 behavior and no charge, price and free-tier quota, no logging/storage, latency profile, and truncation at 2,000 entities with a flag. Annotations already confirm read-only/idempotent, and the description adds operational and cost context that materially affects invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose and alternative tool, then dense operational facts with zero filler. Every sentence carries information an agent needs (pricing, limits, caveats). Not padded despite its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a stateless extraction tool with a full output schema, this covers purpose, alternative, constraints, cost, performance, model caveats, and privacy. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds useful meaning by naming the 18 OntoNotes label types and clarifying the default (all types), and by noting the 20,000-character ceiling's latency implication — this exceeds the schema's raw enum list.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Extract named entities') and names the sibling alternative ('For fields of your own schema use extract_json_with_schema'), so an agent can distinguish it immediately. The entity types are enumerated with concrete examples.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the agent away from this tool to extract_json_with_schema for custom schemas, and names the language constraint ('English only') and model limitations. When to use vs not is clear without opening any schema.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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