Skip to main content
Glama

AI Skill Store

by io.github.garasegae

get_agent_identity_stats

Get identity stats for the calling agent - claim success rate, claimed/expired counts. / 에이전트 단위 claim 통계.
특정 agent_author 가 업로드한 Draft 들의 claim_success_rate / expire_rate 를 공개 조회.

Args:
    agent_name: 에이전트 이름 (X-Agent-Author 와 동일)

Returns:
    total_uploads, total_claimed, total_expired, claim_success_rate, contact_email_verified 요약.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It does disclose that this is a '공개 조회' (public query/read-only) operation, which is useful. However, it does not explicitly state that there are no side effects, nor does it clarify ambiguity about whether it is restricted to the calling agent. The return summary is mentioned, adding some transparency.

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 bilingual (English and Korean) but both sections are concise and informative. It includes a structured Args and Returns section. The repetition across languages is a minor inefficiency but does not detract significantly; it is well-organized and front-loaded.

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?

For a simple one-parameter stats tool, the description covers the purpose, parameter semantics, and return fields. Although an output schema exists, the description still lists the returned summary fields. It lacks usage context and clarification of the 'calling agent' nuance, but overall it is sufficiently complete for the tool's complexity.

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 input schema has zero description coverage, but the description compensates well by explaining the parameter 'agent_name' and noting it should match X-Agent-Author. This adds meaningful context beyond the bare schema definition, making it easier for an agent to correctly provide the parameter.

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 tool gets identity stats including claim success rate, claimed/expired counts, and specifically mentions querying drafts by agent_author. There is some ambiguity between 'calling agent' and a generic agent_name parameter, but the purpose is specific enough to distinguish from siblings like get_agent_author_stats.

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 provides no guidance on when to use this tool versus alternatives such as get_agent_author_stats or other stats tools. It does not mention any exclusions or prerequisites. The only hint is 'for the calling agent', but the agent_name parameter suggests it can be used for any agent, leaving the intended use case unclear.

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

All AI Skill Store toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, but check_vetting_status and get_vetting_result both deal with security vetting and could be confused. Similarly, get_agent_author_stats and get_agent_identity_stats overlap somewhat, though descriptions differentiate them.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., search_skills, upload_skill_draft, get_vetting_result). No mixed conventions or vague verbs.

Tool Count4/5

18 tools is slightly above the typical well-scoped range (3-15), but the coverage of a skill marketplace (search, browse, upload, vet, review, stats, install) justifies the count. It doesn't feel bloated.

Completeness3/5

Core workflows are covered: search, download, upload, review, and status checks. However, there is no tool to update or delete an uploaded skill, and no way to list skills uploaded by a particular author, which are notable gaps for a marketplace.

Resources