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vbcherepanov

total-agent-memory

memory_entity_resolve

Idempotent

Resolve a mention to its canonical entity within a project and type, using name/alias matching and embedding similarity. Returns the entity ID or creates a new one if no match exceeds the threshold.

Instructions

v11.0 W1-F: resolve a mention to its canonical entity within a project+type. Cross-session coreference via name/alias index + embedding cosine. Returns canonical_id, matched_via, and is_new flag. Pronouns return -1.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoEntity type: person, technology, project, company, ...person
mentionYes
projectNogeneral
thresholdNoCosine similarity threshold for embedding match.
create_if_missingNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=false and idempotentHint=true, and the description adds some context about pronoun behavior and matched_via. However, it does not disclose the potential side effect of creating a new entity when create_if_missing is true, which is a significant behavioral aspect not covered by annotations alone.

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 mostly concise and well-structured, packing method, behavior, and return values into a short paragraph. The inclusion of 'v11.0 W1-F' at the start is extraneous and could confuse, but does not significantly harm clarity.

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?

Given the moderate complexity (5 parameters, one required) and no output schema, the description provides sufficient high-level context (return values, special behavior) but lacks detail on parameter semantics and side effects. It is adequate for a basic use case but not comprehensive for edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 40% (type and threshold have descriptions), and the description text does not elaborate on the undocumented parameters (mention, project, create_if_missing). Without additional explanation, an agent may not understand the full meaning of these parameters, especially 'mention' and 'create_if_missing' behavior.

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 clearly states the tool's function: resolving a mention to a canonical entity within a project+type, using cross-session coreference via name/alias index and embedding cosine. It also specifies the return values (canonical_id, matched_via, is_new) and the special case for pronouns returning -1, leaving no ambiguity about purpose.

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 description does not provide any guidance on when to use this tool versus alternatives. It only explains the basic operation without context on ideal scenarios, prerequisites, or when another tool (e.g., memory_save or memory_search_by_tag) would be more appropriate.

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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