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CompletionKit

by com.completionkit

responses_list

List responses for a run, in row order. Returns {total, limit, offset, returned, responses}. Defaults to 50 rows because full payloads are large: use "fields" to drop the bodies, "min_score"/"max_score" to isolate low scorers, and sort "score_asc" to read the worst rows first. For per-metric averages of the whole run use runs_get instead of aggregating here.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoRow order; defaults to "id".
limitNoRows to return; defaults to 50, capped at 500.
fieldsNoOnly return these keys, keeping the payload small. Response keys: id, run_id, input_data, response_text, expected_output, created_at, score, reviewed, reviews, status, attempts, row_index, error. Prefix with "reviews." to trim each review, e.g. ["score", "reviews.metric_name", "reviews.ai_score"]. id is always included.
offsetNoRows to skip before returning results.
run_idYes
statusNoFilter by row status: pending, retrying, succeeded or failed.
max_scoreNoOnly rows whose average judge score is at most this. Use with sort "score_asc" for failure-mode analysis.
min_scoreNoOnly rows whose average judge score is at least this.

TDQS

A4.7/5.0
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 the return envelope, explains the rationale behind the 50-row default (large payloads), and suggests filter/sort combinations for failure-mode analysis. It does not mention pagination caveats or rate limits, but for a read-only list tool the disclosed behavior is substantial and practical.

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 three sentences long, front-loaded with the core purpose, followed by practical usage tips, and ends with an alternative tool reference. Every sentence contributes meaningful information with no redundancy or fluff.

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?

The tool has 8 parameters and no output schema, but the description covers the return envelope, defaults, parameter combination strategies, and an alternative for aggregated metrics. The schema covers parameter-level details (enum values, limits, filter semantics) with high coverage, making the overall description complete for a list operation with practical usage context.

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 description coverage is high (88%), so the schema already documents most parameters. The description adds value by explaining why the default limit is 50, how 'fields' relates to payload size, and how min_score/max_score and score_asc should be combined. This goes beyond the schema's individual parameter descriptions, though run_id remains undocumented in both schema and description.

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 'List responses for a run, in row order,' using a specific verb and resource, and distinguishes itself from runs_get by noting it is for row-level listing, not per-metric averages. This differentiates it from sibling tools like responses_get and runs_get.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool and when not to: it explains the default 50-row behavior, suggests using 'fields' to drop bodies, 'min_score'/'max_score' to isolate low scorers, and 'sort: score_asc' for worst rows. It also explicitly directs users to runs_get for per-metric averages, naming an alternative tool.

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

B3/5.0
Disambiguation5/5

Each tool targets a distinct resource and action, with clear separation across agreements, datasets, judges, metrics, prompts, runs, tags, and usage. Even similar tools like datasets_create vs datasets_create_from_url and runs_generate vs runs_rerun are explicitly differentiated in their descriptions.

Naming Consistency5/5

The overwhelming majority of tools follow a consistent plural_resource_action snake_case pattern (e.g., datasets_create, metrics_update, runs_retry_failures). The only slight deviation is promptfoo_import, but it is still descriptive and does not break the overall predictability.

Tool Count1/5

With 54 tools, the server far exceeds the 25+ threshold considered too many, and approaches the 50+ extreme mismatch level. Even for a broad LLM evaluation platform, this count is excessive and likely to overwhelm agents, making tool selection more error-prone.

Completeness4/5

The toolset provides full CRUD for core resources (datasets, metrics, prompts, runs, tags) plus lifecycle operations like publish, generate, regrade, and retry. It also includes cross-cutting utilities (usage, import, provider credentials). Minor gaps exist, such as no update/delete for agreements and no cross-run response search, but these are non-essential for the primary workflows.

Resources