nittim

Report a Completed Nittim Loop (opt-in, counts only)

report_loop

NEEDS A KEY: mint one at https://nittim.com/keys. Records anonymised counts from a completed self-review pass: pass numbers, a findings-by-category tally, a fixed count, and whether each pass was clean. The schema carries counts only — no title, file path, snippet, or other description of a specific finding. Nothing is charged. Reporting the same repo again from this account updates the existing record with the new numbers instead of being rejected. The reply states which happened (outcome: "inserted" or "updated"). Results appear at https://nittim.com/loop.

Input Schema

TableJSON Schema

Name	Required	Description
`swept`	No	Optional, only meaningful when the loop looked systemic: did you actually sweep the CLASS (a guard, lint rule, or exhaustiveness check that makes a new instance loud by default), not just fix the instances a pass happened to name? Omit if you didn't do a systemic pass or aren't sure — this is never derived from `passes`, only your own claim, and it never changes the reward. On a REPEAT report of the same repo, omitting this KEEPS whatever you last said, rather than clearing it — send `false` explicitly to withdraw an earlier sweep claim.
`passes`	Yes	One entry per pass you actually ran, in order — the same loop public/selfcheck.md walked you through.
`repo_hash`	Yes	sha256 of the repository's canonical identity (e.g. the lowercased 'owner/repo', or a stable local-project fingerprint) — NEVER the repo name itself. Compute this yourself; nittim never sees the name.
`client_name`	No	Your own name, e.g. 'Claude Code' — omit to let nittim read it from the MCP connection if available.
`convergence`	No	Optional: how the loop stopped, if you know — 'converged' (two consecutive clean passes) or 'cap_reached' (stopped for some other reason — a pass budget, a time box, a human call — before converging). Omit if you're not sure; nittim derives its own systemic-pattern read from `passes` regardless of what you say here.
`client_version`	No	Your own version string, if you have one.
`repo_size_bucket`	Yes	A rough size bucket for the repo you looped over: xs, s, m, l, or xl.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 5, 2026

TDQS

A4.4/5.0

Behavior5/5

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

The annotations are sparse (readOnlyHint=false, destructiveHint=false, idempotentHint=false), so the description carries the behavioral burden—and it does so thoroughly. It discloses the key requirement, the no-charge policy, the upsert behavior on repeat reports, the 'inserted'/'updated' outcome in the reply, and the results URL. This goes well beyond what the structured fields provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but efficient: four sentences, each earning its place. It front-loads the critical key requirement, then covers payload shape, privacy, charging, repeat-report behavior, and response outcome. No filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-parameter tool with no output schema, the description covers the essential operational context: authentication, cost, privacy, upsert behavior, and the reply outcome. It relies on the schema for parameter details, which is acceptable given 100% coverage. A slight gap is the lack of error-condition guidance, but it is not critical for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 7 parameters with detailed descriptions. The description adds a useful high-level summary of what counts get recorded, but it does not add per-parameter semantics beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Records') and a specific object ('anonymised counts from a completed self-review pass'), and explicitly limits the payload to counts only. This clearly distinguishes it from sibling audit/scan tools, which focus on running or inspecting audits rather than reporting completion numbers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly indicates this is for post-loop reporting after a self-review pass, and calls out that it is opt-in and requires a key. It does not explicitly name alternatives like audit_repo or scan_source or state when not to use it, but the 'completed self-review pass' framing provides strong contextual guidance.

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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TDQS

A4.3/5.0

Disambiguation4/5

Most tools map to distinct workflow stages: estimate, preview, submit audit, retrieve audit, run module, dispute finding, and judge output. The closest pairs are audit_repo vs audit_source and scan_source vs run_module, but the descriptions provide enough boundary detail for an agent to choose correctly.

Naming Consistency5/5

Every tool follows the same snake_case verb_noun pattern: audit_repo, audit_source, estimate_audit, get_audit, list_modules, run_module, scan_source, and so on. There is no mixing of naming conventions or vague standalone verbs.

Tool Count5/5

11 tools is well-scoped for a paid AI audit service with async delivery, approval flows, and both repo-based and source-based inputs. Each tool covers a distinct step in the audit pipeline without feeling redundant or bloated.

Completeness4/5

The core lifecycle is covered: estimate cost, submit audits, run single modules, check status, retrieve results, and dispute findings. Minor gaps include no way to list past audits, cancel a pending audit, or manage credits directly, though estimate_audit does show the balance.

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