Skip to main content
Glama

concordance

candidate_commit

Candidate Engine step 1 — commit a COMPLETE raw candidate set BEFORE any evaluation. Give the query, the generator, the method, and EVERY raw candidate; weights ride verbatim as UNTRUSTED metadata (proposal_weight is never confidence and never ranks anything). Returns the committed set: a content-addressed candidate_set_id, the membership commitment hash (also sealed as a durable anchor), and per-candidate ids — all born quarantined. Nothing is verified here; that is the point: a set hashed before checking cannot be selectively disclosed after.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesthe user request the candidates answer (hashed; not stored raw)
promptNothe exact generation instructions (hashed, not stored)
generatorYesmodel/provider/version or human source (provenance)
candidatesYesthe COMPLETE raw generator output — omit nothing
generation_methodYeshow the set was produced

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the set is hashed before checking, weights are untrusted metadata, output is quarantined, and nothing is verified. This is substantial transparency. It stops short of describing side effects like immutability or the durable anchor's implications, which would push it to 5.

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 four sentences, but each carries essential information: what to submit, the weight warning, return values, and the rationale for hashing before verification. It is front-loaded with the core purpose and avoids redundancy, making it appropriately concise for the complexity.

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

Completeness5/5

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

Despite no output schema, the description explains the return values (candidate_set_id, commitment hash, per-candidate ids) and the quarantine state. It covers the essential context for an agent to understand the full lifecycle of this commit step, including the cryptographic commitment and the 'cannot be selectively disclosed' property. This is sufficient for a tool of this complexity.

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

Parameters4/5

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

Schema coverage is 100%, providing baseline 3. The description adds value by clarifying the semantics of proposal_weight ('never confidence and never ranks anything') and emphasizing that the candidates array must contain EVERY raw candidate. This goes beyond the schema's basic descriptions, earning a 4.

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 clearly states the specific action: 'commit a COMPLETE raw candidate set BEFORE any evaluation.' It identifies the resource (raw candidate set), the verb (commit), and distinguishes itself as step 1 in a pipeline, separate from evaluation. This differentiates it from siblings like candidate_get or candidate_narrow.

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 phrase 'Candidate Engine step 1' and 'BEFORE any evaluation' clearly indicate when to use this tool. It also emphasizes supplying the query, generator, method, and every raw candidate. However, it does not explicitly mention alternatives or exclusionary conditions, such as 'do not use after evaluation,' so it lacks a fully explicit when-not-to-use clause.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.6/5.0
Disambiguation2/5

Several tools are near-duplicates: read_passage and resolve both fetch WEB text for a reference; word_study already includes every occurrence that word_occurrences returns; coach_next and coach_recommend both answer 'what's next.' Search/locate/cards_browse also overlap as discovery entry points, making tool selection ambiguous despite detailed descriptions.

Naming Consistency3/5

Most names follow an object_verb snake_case pattern (cards_browse, study_create, seal_fetch), but there are many bare verbs/nouns (ask, audit, resolve, verify, canon, harmony) and inconsistent singular/plural pairs (card_get vs cards_browse, group_create vs groups_list, want_open vs wants_list). No camelCase, but the convention is not uniform.

Tool Count1/5

86 tools is an extreme count for any single MCP server, far beyond the 3-15 well-scoped range; even a broad platform would be hard for an agent to navigate. Many tools belong to unrelated subdomains (coach, steward, mesh, calendar), making the surface unwieldy.

Completeness2/5

The want/offer flow has no accept/close tool, so an agent can open a want and offer a source but never see it resolved. Group and calendar coverage are one-directional (create/join only; no leave/delete/list/update), and there is no badge listing or way to update a study group. Core reading/verification/shelf flows are solid, but lifecycle gaps remain.