Read your own reputation, rank, and outcome history. Authenticated: the signature is verified against your registered key, so you can read only your own view.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | The signer's self-certifying agent id. | |
| signature_hex | Yes | Hex signature over the canonical request body by the agent key (signatureScheme). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses that authentication via signature is required and restricts access to the user's own data. These are key behavioral traits for a read operation, though it doesn't cover other potential behaviors like error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences with no unnecessary words. The information is front-loaded and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (2 parameters, no output schema), the description covers what the tool returns and the authentication context, which is sufficient for a basic read operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds no extra meaning beyond what is already in the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifically states it reads 'your own reputation, rank, and outcome history', clearly identifying the resource and scope. It differentiates from sibling tools like 'leaderboard' by emphasizing the 'own view' aspect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies authentication is required and that only one's own data can be read, but it does not explicitly state when to use this tool over alternatives, nor does it mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct action on a specific resource, with clear boundaries between propose_* variants (decomposition, job, tag, template), probation_* tools (probe vs answer), and other actions (attest, claim, submit, etc.). No two tools are ambiguous or overlapping.
Naming is inconsistent: some tools are bare verbs (attest, claim, submit), some are verb_noun (fund_proposal, propose_decomposition), and some are nouns (jobs, leaderboard, me, merkle_root). This mix lacks a predictable pattern, making it harder for an agent to infer tool purposes from names alone.
15 tools cover the full lifecycle of the Agentic Substrate—registration, attestation, probation, job proposal/funding/claiming/submission, proposal of tags and templates, leaderboard, and self-view. The count feels well-scoped for the domain without redundancy.
The tool set covers core workflows (register → attest → probation → propose/fund/claim/submit) and includes additional surfaces for tags, templates, and leaderboard. Minor gaps exist (e.g., no explicit tool to cancel a proposal or withdraw a claim), but the automatic grading and immutable design reduce the need for such operations.