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Glama

get_entity

Fetch all stored facts and relationships about a named entity, enabling context-rich answers or data retrieval.

Instructions

Get everything known about a named thing — who or what it is, the facts about it, and how it connects to other things.

A "thing" is any subject this store holds knowledge about: a person, an organisation, a project, a place, a product, a system — whatever the store is used for. This is the "tell me about X" tool.

Call this WHENEVER a named thing is mentioned or alluded to and knowing it would improve your answer — including indirect references like "my manager", "the Helsinki office", "the payments service". Call it before advising on anything involving that thing, and before asking who or what something is — you may already know.

Facts come back most relevant first, each flagged with is_subject. True means the fact is ABOUT this thing; false means it only mentions it. Treat the difference as real when you answer: "Alex's transfer was approved by Robin" is worth knowing when asked about Robin, but it is a fact about Alex, and reporting it as something you know about Robin would be wrong. Other relationship values are the same kind of role — this entity's part in this fact, free text, not a directed graph edge. Do not infer who did what to whom from the wording.

If several entity rows share that name under different types (the extractor labelled one thing two ways), facts from all of them come back. Hyphens, underscores, and stray punctuation count as the same letters only when that does not join two names already stored as separate rows. If this store has no entity by that name, facts that mention the wording still come back (is_subject false) rather than an empty miss. found is whether an entity row exists, not whether anything is known.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe thing's name. Resolve an indirect reference to a name first if you can (e.g. via get_context or a prior fact).
typeNoOptional type filter, only for disambiguation when one name refers to two different things (a person and a project both called 'Mercury'). Types are whatever this store uses — omit it to match any type, which is almost always what you want.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.30.1

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description fully discloses edge cases: multiple types sharing a name return combined facts, punctuation normalization rules, missing names returning mention facts with is_subject false, and the definition of 'found'. This gives the agent a precise behavioral model.

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?

While long, every sentence adds essential information about behavior, edge cases, or parameter interaction. The main purpose is front-loaded, and subsequent details are logically organized, earning their place without redundancy.

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?

Given no output schema, the description explains return semantics (facts, is_subject, found) sufficiently. It also addresses ambiguity, missing entities, and type variance, making the tool's behavior fully predictable for an agent.

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 descriptions cover both parameters thoroughly (name resolution guidance, type as a disambiguation filter). The description reinforces type behavior ('facts from all of them come back') and adds context on resolving indirect references, slightly exceeding the high coverage baseline.

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 ('Get') and resource ('everything known about a named thing'), and clearly distinguishes itself as the 'tell me about X' tool. It contrasts implicitly with sibling tools by focusing on entity retrieval with fuzzy matching and is_subject semantics.

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?

Provides explicit when-to-use guidance: 'Call this WHENEVER a named thing is mentioned or alluded to' and 'Call it before advising on anything involving that thing, and before asking who or what something is.' It also explains behavior for missing entities and ambiguous names, reducing guesswork.

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