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Glama

PersonalKnowHow

by io.github.Georgi-Petkov

Find skill evidence

skill_evidence
Read-only

Given an exact tag/skill (e.g. 'docker', 'gcp'), returns EVERY entry with that tag, uncapped, grouped by type with a real count per type. Unlike related_entries (capped at 15, requires a starting entry id) or query_knowhow (semantic, ranked, may over- or under-include), this is an EXACT tag match against every entry -- the right tool for 'how many X have I completed/done' or 'do I have any real evidence for X at all'. Tags are exact strings from a prior list_by_type/related_entries/query_knowhow result's tags array -- this is NOT semantic search; a tag never assigned during ingest returns found:false, try query_knowhow instead. Each type's entries sort by captured_at ascending (oldest first); entries with no captured_at are moved to the end and counted in undated_count, never silently sorted as if their date were known.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagYesAn exact tag from a prior result's tags array, e.g. 'python', 'docker', 'gcp'

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses exact-match semantics, uncapped results, grouping by type with real counts, sorting behavior by captured_at, undated_count handling, and the found:false response for unmatched tags. This is rich behavioral context that annotations alone do not provide.

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?

Every sentence delivers essential information: main behavior, sibling contrasts, usage guidance, exact-match caveats, and sorting/undated_count semantics. The description is dense but focused, front-loading the core behavior before differentiation and edge cases.

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?

With no output schema, the description compensates by explaining return semantics: grouped entries, counts, sorting, undated_count, and found:false. It covers the main usage scenarios and edge cases effectively, leaving no critical gap for an agent to call it correctly.

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 description coverage is 100%, so baseline is 3. The description adds extra meaning by emphasizing that the tag must be an exact string from a prior result's tags array and explicitly warns 'this is NOT semantic search', reinforcing correct parameter usage beyond the schema.

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 action and resource: 'returns EVERY entry with that tag, uncapped, grouped by type with a real count per type.' It clearly distinguishes itself from siblings by naming related_entries and query_knowhow and explaining their differing behavior.

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?

Explicit when-to-use guidance is provided: 'the right tool for "how many X have I completed/done" or "do I have any real evidence for X at all".' It also names alternatives and their limitations, and tells the agent to try query_knowhow if an exact tag returns found:false.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a clearly distinct retrieval mode: exhaustive listing by type, semantic ranked search, relationship traversal by ID, and exact tag matching. The descriptions actively cross-reference each other to prevent confusion, making misselection very unlikely.

Naming Consistency3/5

All names are snake_case and readable, but the pattern is mixed: list_by_type and query_knowhow are imperative verb phrases, while related_entries and skill_evidence are noun phrases. A more consistent set would use verbs throughout, such as get_related_entries and get_skill_evidence.

Tool Count5/5

Four tools is well-scoped for a read-only personal knowledge graph. Each tool covers a necessary and non-redundant query path, and none feel like filler or missing from the core set.

Completeness4/5

The set covers the main query needs: exhaustive lookup by type, semantic search, related entries, and exact skill evidence. Minor gaps exist, such as no direct get-entry-by-id tool and no way to enumerate all available types or tags without first running a search, but these are workable.

Resources