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anythink-cloud

Anythink-MCP

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Search: similar

search_similar
Read-only

Find documents similar to a given record using vector search. Provide entity and source document ID to retrieve related results, with optional result limit and public-only fields.

Instructions

Find documents similar to a given record (vector search)

Input Schema

TableJSON 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

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds the meaningful detail that matching is vector-based (semantic) rather than keyword-based, which changes how an agent should interpret results. It does not mention scoring, ranking, or result shape.

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?

A single tight sentence with zero waste, front-loading the action and the source record. Every word earns its place.

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

Completeness3/5

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

For a read-only search tool with a fully documented schema and safety annotations, the description is minimally adequate. With no output schema, it still doesn't hint at the return format (ranked list, score field) or pagination, which an agent would benefit from knowing.

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 description coverage is 100%, so every parameter (id, entity, limit, public) is already documented in the schema. The description adds no syntax or semantic detail beyond what the schema provides, making the baseline 3 appropriate.

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?

States a specific verb (Find) and resource (documents) with the qualifier 'similar to a given record', and adds the mechanism '(vector search)'. However, it offers no differentiation from siblings like search_query, search_rehydrate, or search_purge, so an agent cannot distinguish intent from the description alone.

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

Usage Guidelines2/5

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

The phrase 'similar to a given record' implies a use case, but there is no explicit when-to-use guidance, no exclusions, and no reference to the alternative search_* tools. The agent is left to infer when semantic similarity lookup is preferable to keyword search.

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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