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diffbot-mcp

diffbot-mcp

Official
by diffbot
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Resolve Entities

resolve_entities
Read-onlyIdempotent

Extract and link people, organizations, places, and products in plain text to Diffbot Knowledge Graph entities with confidence, salience, and sentiment.

Instructions

Named entity recognition and linking. Finds people, organizations, places, and products mentioned in text. Each resolves to a Diffbot Knowledge Graph entity with confidence, salience, and sentiment. Prefer for post-processing plain text for entity mentions. The response includes dql_filter, an id:or(...) clause for dql to fetch the full records. Entities without an id were recognized but not matched. API docs: https://www.diffbot.com/docs/natural-language/

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "resolve_entities"
  }
}

Input Schema

NameRequiredDescriptionDefault
langNoISO code, e.g. en; default auto
textYesPlain text to analyze

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, openWorld, non-destructive), and the description goes well beyond them: it explains that each mention carries confidence, salience, and sentiment, that the response contains a ready-made dql_filter id:or(...) clause reusable with the dql sibling, and that entities lacking an id were recognized but not matched. That last note is exactly the kind of non-obvious semantics an agent needs to interpret results.

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

Conciseness4/5

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

The purpose is front-loaded in the first two sentences and each subsequent sentence adds a distinct operational fact (linked fields, dql_filter, unmatched-entity semantics, docs link). Slight redundancy between the opening label and the 'Finds people, organizations...' sentence keeps it from a 5.

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?

With an output schema present, the description need not restate return values, and it still flags the two things an agent would otherwise misread: the dql_filter hand-off to the dql tool and the meaning of a missing id. Nothing needed to call or interpret this tool is absent.

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

Parameters3/5

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

Schema coverage is 100% and both parameters (text, lang with auto default) are already documented in the schema. The description only reinforces that the input is 'plain text' and adds no format, size, or language-handling detail beyond the schema, so the baseline 3 applies.

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

Purpose4/5

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

The description gives a specific verb+resource ('Named entity recognition and linking') and enumerates the entity types found in text, so the operation is unmistakable. It doesn't explicitly name which sibling to use instead, but the 'post-processing plain text' framing implicitly separates it from the URL-oriented extract/search tools.

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

Usage Guidelines4/5

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

'Prefer for post-processing plain text for entity mentions' is a clear when-to-use cue that positions the tool after extraction rather than as a crawler or search entry point. It stops short of stating when NOT to use it or naming a concrete alternative.

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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