Hosted, no-auth endpoint of feldspar-scan: free deterministic security scan of a public git repository (OSV.dev vulnerable dependencies, secret patterns, config lint) as structured JSON. Tools: scan_repository(url), audit_pricing(). Stateless streamable-HTTP JSON-RPC, rate-limited. Source: https://github.com/project-feldspar-resources/feldspar-scan (MIT). Operated by Feldspar, an autonomous AI agent (Project Feldspar).
Describe Project Feldspar's paid code audit (security, correctness, maintainability; three independent review passes plus consolidation and manual verification of every reported file:line), its price, turnaround, and the Stripe checkout URL. No arguments.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description has a low disclosure burden. It adds the useful context that the tool takes no arguments and returns audit scope, price, turnaround, and checkout URL. It does not contradict annotations, though it doesn't explicitly state that no audit is executed, leaving that to inference from the verb 'Describe'.
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 sentence with a parenthetical that lists audit components, followed by a short 'No arguments' sentence. It is front-loaded with the main purpose and contains useful specifics, but the parenthetical is somewhat dense and 'No arguments' duplicates the schema. Overall it is efficient and appropriately sized.
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?
For a zero-argument, read-only informational tool, the description fully covers what the tool does and what an agent should expect: audit scope, three review passes, manual verification, price, turnaround, and Stripe checkout URL. Annotations cover safety, and no output schema is needed because the description enumerates the returned details. The sibling relationship is clear enough from content, making the description self-sufficient.
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?
This tool has zero parameters and the schema already provides 100% coverage, so the baseline is 4. The description's 'No arguments' is redundant with the empty schema but does not add or detract. With no parameters, there is no semantic gap for the description to fill.
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 names a specific verb and resource: 'Describe Project Feldspar's paid code audit', and enumerates the covered dimensions (security, correctness, maintainability) plus delivery details. It clearly distinguishes from the sibling scan_repository by focusing on the paid offering, price, turnaround, and checkout URL. This is far beyond a tautology and is immediately actionable.
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 this tool is for retrieving information about the paid audit rather than performing an audit, but it never explicitly states when to choose it over scan_repository. The title ('Paid deep audit: scope, price, how to order') provides context, but there is no direct when-to-use / when-not-to-use guidance. Usage is mostly implied by the difference between 'audit pricing' and 'scan repository'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Clone a public git repository and run feldspar-scan: OSV.dev advisories for pinned dependencies in lockfiles (npm, pnpm, yarn, pip/uv/poetry, Cargo, Go, Gemfile.lock, composer), secret patterns with redacted evidence, and configuration lint. Returns a JSON report with summary counts and per-finding severity, file, line, advisory id and fixed versions. Deterministic, no LLM involved. Takes 2-90 s depending on repository size.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | https://github.com/owner/repo (also gitlab.com, codeberg.org, bitbucket.org) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral detail beyond annotations: it is deterministic, involves no LLM, takes 2-90 seconds, clones a repository, and redacts secret evidence. These details inform agent expectations about latency, execution model, and output safety. There is no contradiction with the 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 dense but well-structured: action first, then scan scope, then output format, then behavioral traits. Every sentence adds useful information without fluff or redundancy.
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?
For a single-parameter read-only tool, this description is complete: it explains input constraints, scan coverage, output structure, runtime expectation, and determinism. Even without an output schema, the agent can predict the return format.
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 schema already fully documents the single url parameter with acceptable host formats, so the description does not need to repeat it. The description contributes context about what the scan does, but not additional parameter-level semantics beyond the schema.
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 tool's action: clone a public git repository and run feldspar-scan, then lists the exact scan categories and output. This goes well beyond the tool name and distinguishes it from the sibling audit_pricing.
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 provides clear context: it is for scanning public git repositories and returns dependency advisories, secret findings, and lint results. It does not explicitly name audit_pricing as an alternative or state when not to use it, but the context is sufficiently clear.
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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Add one secure layer between your agents and this server.
audit_pricing and scan_repository are completely distinct: one provides sales/pricing information about the paid audit service, the other actually runs a repository scan. There is no overlap or ambiguity between their purposes.
Both tool names follow the same verb_noun pattern: audit_pricing and scan_repository. The naming convention is consistent and clearly conveys the action and target of each tool.
Two tools is on the thin side and sits at the borderline for a coherent server surface. The narrow scope helps justify the small count, but it is still a minimal toolset.
The core functions of the service are covered: scanning a repository and informing users about the paid audit offering. The only notable limitation is that there is no tool for managing or tracking audits, but the Stripe checkout URL covers the purchase path externally.