plith

dedupq_check

Idempotent

Before executing any LLM task, check if an identical or semantically similar task has already been completed. Returns cached result on hit, saving one LLM call. On a miss, execute your task and call dedupq_complete to cache the result for future hits. Costs 1 credit.

Input Schema

TableJSON Schema

Name	Required	Description
`content`	Yes	The task content to check for duplicates. This is hashed and embedded for matching.
`task_id`	No	Optional caller task ID for tracing and cross-referencing with BurnRate.
`hash_only`	No	If true, skip vector similarity search and use exact hash matching only. Default: false.
`similarity_threshold`	No	Cosine similarity threshold for semantic matching, 0.0 to 1.0. Default: 0.80.

Output Schema

TableJSON Schema

Name	Required	Description
`match`	No
`status`	No	hit \| miss \| in_progress
`cache_hit`	No
`request_id`	No
`content_hash`	No
`credits_used`	No
`cache_age_seconds`	No
`credits_remaining`	No
`fallback_behavior`	No

TDQS

A4.2/5.0

Behavior4/5

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

The description discloses the hit/miss behavior, the cost of 1 credit, and the complementary dedupq_complete flow, adding value beyond the annotations (readOnlyHint=false, idempotentHint=true). It does not mention potential side effects on the cache or error conditions, but those are not critical given the simple check purpose.

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?

Three sentences, each conveying distinct information: purpose, hit/miss behavior, and cost. No unnecessary words 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?

The description covers the core workflow (check, execute, complete), mentions cost, and relies on the output schema for return values. It is adequate for a tool with 4 parameters and 100% schema coverage, though it could mention edge cases like cache misses on similar content or stale results.

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 baseline is 3. The description does not add significant parameter semantics beyond what the schema already provides, such as the hashing behavior for content, which is already in the schema 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 that the tool checks for identical or semantically similar tasks before executing LLM tasks, and differentiates from dedupq_complete by explaining that on a miss, the user should execute the task and call dedupq_complete to cache the result.

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 explicitly says 'Before executing any LLM task, check...' providing clear when-to-use. It also gives an alternative—calling dedupq_complete after a miss—but does not mention explicit conditions when not to use the tool, so it falls short of fully explicit when-not 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

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, grouped by functionality (cost, dedup, guardrails, pitfalls, quality, rigor). Descriptions clearly differentiate within groups, so an agent can reliably select the correct tool.

Naming Consistency5/5

All tool names follow a consistent pattern of lowercase with underscores, using category prefix (burnrate_, dedupq_, guardrail_, etc.) followed by a descriptive verb or noun. No naming mix-ups.

Tool Count5/5

15 tools cover multiple distinct capabilities (cost management, deduplication, guardrails, pitfalls, quality, workflows) without being excessive. Each tool has a clear role, and the count is appropriate for the server's scope.

Completeness4/5

The tool surface covers core workflows well, but there are minor gaps: no tool to delete/update guardrail policies, no explicit qualitygate rule management, and no workflow cancellation tool. Also, 'qualitygate_trends' is referenced but not provided as a tool. Overall, agents can work around these gaps.

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