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Glama

aleph_similar_entities

Read-only

Find candidate matches for an entity across datasets to link records, like a leak subject to a company registry. Verify ranked candidates using birth dates, addresses, or registration numbers.

Instructions

Find entities that may be the same real-world person or company as this one, across other datasets. Use it to link a subject between a leak and a company registry. Results are ranked candidates, not confirmed matches — verify with birth dates, addresses or registration numbers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax candidates to return.
entity_idYesEntity ID to find matches for.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish read-only, open-world, and non-destructive behavior. The description adds valuable context beyond that by stating results are 'ranked candidates, not confirmed matches' and advising verification with birth dates, addresses, or registration numbers. This meaningfully informs downstream agent 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?

The description is three concise sentences with no filler. It front-loads the purpose, then gives a practical use case, and closes with an important caveat. Every sentence earns its place.

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?

There is no output schema, but the description explains that the tool returns ranked candidates rather than confirmed matches and gives verification guidance. It does not describe the exact result shape, but for a simple read-only lookup with two parameters, the provided context is largely sufficient.

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% with descriptions for both parameters: 'entity_id' and 'limit'. The description only indirectly refers to entity_id as 'this one' and does not add meaningful parameter-level detail beyond the schema, so the baseline score of 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 clearly states the operation ('Find') and the resource ('entities that may be the same real-world person or company as this one'), and gives a concrete use case linking a leak to a company registry. It distinguishes the tool from simple entity retrieval, but it does not explicitly contrast it with siblings like aleph_expand_entity or aleph_xref_results.

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?

The description provides explicit guidance on when to use the tool: 'Use it to link a subject between a leak and a company registry.' It also sets expectations that results are candidates, not confirmations. However, it does not state when not to use it or mention alternative sibling tools.

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