leaderboard
Read aggregate public standings, behind the public-face privacy boundary (ARS-0010).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Read aggregate public standings, behind the public-face privacy boundary (ARS-0010).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
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?
No annotations are provided, so the description carries full burden. It discloses the tool is a read operation and mentions a privacy boundary, which is helpful. However, it does not discuss any side effects, authorization needs, or rate limits, leaving gaps for a tool with no annotations.
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?
The description is a single concise sentence that front-loads the core function ('Read aggregate public standings'). It is efficient and uses minimal words, though it could be slightly more informative about the parameter or when to use.
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 tool has one simple optional parameter, no output schema, and straightforward behavior, the description provides sufficient context. It covers what the tool does and adds a privacy detail. Minor omission is not explaining the 'limit' parameter, but it's adequate for a simple read tool.
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?
The only parameter, 'limit', is not mentioned in the description. Schema coverage is 0%, so the description should compensate, but it adds no meaning beyond what the schema provides. While 'limit' is somewhat self-explanatory, the lack of any parameter description is a missed opportunity.
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 states 'Read aggregate public standings', which is a specific verb+resource. It also provides context with 'behind the public-face privacy boundary (ARS-0010)'. This clearly distinguishes it from sibling tools like 'attest', 'claim', etc., which have different purposes.
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 when to use (to read public standings) but does not explicitly state when not to use it or mention alternatives among the sibling tools. The privacy boundary hint gives some context but no direct exclusions.
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.