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share_attribution_report

Generates a report summarizing public contribution-share proof from the local MCP event ledger, optionally filtered by GitHub username.

Instructions

Share Attribution Report - summarize public contribution-share proof from the local MCP event ledger.

Args: username: optional GitHub username filter

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It mentions the report is 'public' and derived from a 'local MCP event ledger', implying a read-only operation, but does not explicitly confirm it is non-destructive, state whether authentication is required, or describe any side effects. The agent needs more certainty.

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 very concise, consisting of two short sentences and a parameter line. It is front-loaded with the main purpose. However, it could be slightly more structured by separating the overall function from the argument explanation. Still, it is efficient without unnecessary words.

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 tool is simple with one optional parameter and an output schema exists (external), the description provides the core idea but lacks deeper context. It does not explain what 'public contribution-share proof' means, nor does it mention return value structure or pagination. For a tool with many siblings, more context would help the agent decide when to use it.

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 description adds meaningful context to the single parameter, stating it is an optional 'GitHub username filter'. The schema only provides the title 'Username' and a default. The description clarifies the source (GitHub) and the filtering purpose, which helps the agent correctly use 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's purpose: to summarize public contribution-share proof from the local MCP event ledger. The verb 'summarize' and the resource 'contribution-share proof' provide specific action and object, distinguishing it from related tools like record_share_attribution which likely creates such proof.

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?

No explicit guidance on when to use this tool versus alternatives. The description does not mention when to choose it over sibling tools like public_proof_pack or other report tools, nor does it specify prerequisites or exclusions. The agent is left to infer usage from the name and brief description.

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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