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faf_bench

Read-only

Measure how much the project.faf context improves answer accuracy on your repo. Generates questions from the .faf answer key, grades answers without and with the .faf, and returns the lift with a receipt.

Instructions

Prove the .faf earns its place — measure how much the context is worth, on THIS repo, falsifiably. Questions derive from the project.faf's own populated slots (the .faf is the answer key), so grading is mechanical — no judge, no rubric. action=questions returns the answer-key-safe question set; action=grade takes your answers WITHOUT the .faf (cold) and WITH it (faf), grades both, and returns the cold→with-faf lift with a ✪ receipt. The delta is the product; the cold number belongs to the absence of context, never to FAF.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fafNoaction=grade: answers produced WITH the project.faf in context. Map of question number → answer string.
coldNoaction=grade: answers produced WITHOUT the .faf (general repo knowledge only). Map of question number → answer string.
pathNoProject path (optional — current directory if omitted).
modelNoaction=grade (optional): the model that produced the answers.
actionNoquestions = get the answer-key-safe question set to answer; grade = submit cold + with-faf answers to score the delta. Default: questions.
fafTokensNoaction=grade (optional): tokens spent answering with the .faf.
coldTokensNoaction=grade (optional): tokens spent answering cold.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
fafNoaction=grade — score WITH the .faf.
coldNoaction=grade — score WITHOUT context (absence baseline).
deltaNowith-faf minus cold — the product.
totalNoNumber of questions in the set.
actionNo
receiptNo✪ receipt — sha256 over the canonical projection; third-party verifiable.
versionNo
protocolNoin-session — answers are self-reported by the agent under test.
qsetHashNoHash of the question set — rides the receipt; same .faf reproduces it.
questionsNoaction=questions only — NEVER includes the answer key.
Behavior4/5

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

Annotations declare readOnlyHint=true and destructiveHint=false, consistent with a non-destructive benchmark. The description adds behavioral context: grading is mechanical (no judge/rubric), and responses are keyed to the .faf. This exceeds the annotations' information.

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

Conciseness2/5

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

The description is overly poetic and verbose ('Prove the .faf earns its place', 'cold→with-faf lift with a ✪ receipt'), which sacrifices conciseness. Critical information is present but not front-loaded.

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

Completeness3/5

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

Covers actions and parameters, but lacks explicit example usage or description of the output format (though output schema exists). The mention of a '✪ receipt' is vague.

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% with descriptions for all 7 parameters. The description adds workflow context (e.g., 'cold' answers without .faf, 'faf' with it) that clarifies parameter usage beyond the schema. However, some repetition exists.

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

Purpose4/5

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

The description clearly states the tool measures how much context (the .faf) is worth, distinguishing it from siblings by focusing on falsifiable benchmarking. However, the metaphorical language ('Prove the .faf earns its place', 'cold→with-faf lift with a ✪ receipt') may cause confusion.

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

Usage Guidelines3/5

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

It explains the two actions (questions, grade) and when to use each, but does not explicitly state when to avoid this tool in favor of siblings like faf_score or faf_trust. The guidance is adequate but not comprehensive.

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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