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anythink-cloud

Anythink-MCP

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search_similar
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Find documents similar in meaning to a given record using vector search by entity and ID, returning the closest matches (10 by default).

Instructions

Find documents similar to a given record (vector search). Takes an entity and a record id and returns the records closest in meaning to it, 10 by default, using vector embeddings in the search index. The public option searches as an anonymous visitor and returns only publicly searchable fields. To find records matching words you supply, use search_query. Returns JSON.

Input Schema

NameRequiredDescriptionDefault
idYesSource document ID — find documents similar to this one
limitNoMaximum results (default: 10)
entityYesEntity name
publicNoUse the public endpoint (only public-marked fields returned)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.27

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover safety (readOnlyHint=true, openWorldHint=false), and the description adds meaningful behavior: vector embeddings in the search index, a default of 10 results, and that the public option acts as an anonymous visitor returning only public fields. It stops short of detailing ranking, score, or pagination behavior.

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?

Four front-loaded sentences with no filler: purpose and mechanism first, then default, then the public caveat, then the sibling routing. Every sentence carries distinct information.

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

Completeness4/5

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

For a read-only vector search with no output schema, the description covers what it returns (records closest in meaning, JSON) and the key mode difference. The absence of any return-shape detail is acceptable given the readOnly annotations and simple result set.

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%, so the schema already documents id, entity, limit (default 10), and public. The description restates the default count and the public-field constraint but adds no syntax or format 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.

Purpose5/5

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

States a specific verb and resource ("Find documents similar to a given record"), identifies the mechanism (vector search), and explicitly distinguishes itself from the sibling search_query. An agent can select this without opening either schema.

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?

Names the alternative tool (search_query) and the exact condition that routes to it ("To find records matching words you supply"), plus clarifies the public/anonymous mode. When-to-use is explicit rather than inferred.

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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