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Resolve a phrase (or emem:entity: token) to a canonical object

emem_entity_resolve
Read-onlyIdempotent

Resolve a fuzzy phrase into an already-registered entity token, ensuring agents co-refer to the same identity instead of re-minting divergent ones.

Instructions

Converge a fuzzy phrasing onto the canonical object other agents already minted, so everyone co-refers to the same identity instead of re-minting divergent ones. Pass text (e.g. "the collapsed span at the ford") to get ranked existing candidates; pass near to narrow to a place; or pass an emem:entity: token to dereference it directly to the signed entity body. Read-only.

When to use: Call BEFORE minting when another agent may already have registered the object, or when you receive a emem:entity: token and want the object behind it. This is how two agents avoid referential drift: resolve first, mint only if nothing matches.

Example arguments: {"text":"the golden gate bridge","near":"San Francisco"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoMax candidates (default 10).
nearNoOptional place/cell to narrow to objects anchored nearby.
textNoFuzzy phrasing to resolve to an existing canonical object (e.g. "the damaged bridge near the river").
labelNoAlias for `text`.
tokenNoA `emem:entity:<entity_cid>` handle to dereference directly to its signed object (bypasses the text search).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral specifics: returns 'ranked existing candidates' for text input, 'narrow to a place' with near, and 'dereference it directly to the signed entity body' for a token. This goes beyond the structured safety hints by explaining the two execution paths and their outputs.

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 description is organized into three paragraphs: purpose/modes, when-to-use, and an example. Each section has a distinct function and avoids redundant detail. The only slight redundancy is 'Read-only,' which duplicates the readOnlyHint annotation, but it does not bloat the description.

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?

Despite lacking an output schema, the description clearly states what callers can expect: ranked candidate objects for text searches and the signed entity body for token dereference. The usage guidance and examples cover the main invocation patterns. The tool's complexity (two modes, 5 optional parameters) is adequately addressed.

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?

The schema provides 100% coverage of all five parameters, so the baseline is 3. The description adds meaningful usage semantics by explaining how text, near, and token interact: text triggers fuzzy search, near narrows by location, and token bypasses the search for direct dereference. It also gives a concrete example. However, it does not explain the k (max candidates) parameter, which remains schema-only.

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?

The description states a specific verb ('Converge'/'Resolve') and resource ('canonical object'), and explains the two modes: fuzzy text resolution and direct token dereference. This distinguishes it from siblings like emem_entity (minting) and emem_memory_token_resolve (general memory tokens).

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?

The 'When to use' section explicitly instructs to call before minting when another agent may have registered the object, or when receiving an emem:entity: token. It also states 'resolve first, mint only if nothing matches,' providing a clear when-not and alternative.

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