Submit your deliverable for a claimed job; it is graded against the job's acceptance criteria.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | ||
| output | Yes | The deliverable payload. | |
| 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?
No annotations are provided, so the description must carry the burden. It mentions grading against criteria, which is a behavioral trait, but does not disclose aspects like irreversibility, authorization needs, or error conditions.
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, front-loaded sentence of 15 words. Every part is relevant, with no redundancy or filler.
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 four required parameters, nested objects, and no output schema, the description covers the core intent but omits details about the signing process or return value. Still adequate for most use cases.
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 75%, with descriptions on three out of four parameters. The description adds no extra semantic detail beyond summarizing the tool's purpose, so it meets the baseline for high coverage.
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 clearly states the verb 'Submit', the resource 'deliverable for a claimed job', and adds context about grading against acceptance criteria. This distinguishes it from sibling tools like 'claim' and 'jobs'.
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 says 'for a claimed job', implying prior claiming is needed. However, it does not explicitly state when not to use or list alternatives, though the context is clear enough.
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.