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by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,721 across 1497 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the annotations by disclosing the grounded extraction behavior, the exact success/refusal response shapes, and the specific refusal_reason values. It also clarifies that answers are derived ONLY from tool results, which directly supports the readOnlyHint and openWorldHint annotations. No contradiction exists.

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

Conciseness4/5

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

The description is fairly long but every sentence carries material information: behavior, return format, refusal reasons, use cases, and cost comparison. It is front-loaded with the core purpose and only loses a point for being somewhat dense relative to the minimum needed.

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?

Given the tool's complexity, the absence of an output schema, and the nuanced refusal behavior, the description is complete. It covers the invocation context, the routing mechanism, the return structure, the failure modes, and the cost tradeoff—nothing essential is missing for correct selection and 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 fully documents the parameters. The description explains the overall query mechanism ('picks the right tool...fills arguments') but does not add specific parameter-level meaning beyond what the schema already provides. This meets the baseline for full schema coverage.

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 a specific verb ('ask'), resource ('Pipeworx'), and mode ('grounded'), and explicitly differentiates it from ask_pipeworx by highlighting hallucination resistance and evidence extraction. An agent can immediately understand what this tool does and how it differs from its sibling.

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 explicitly states when to use this tool ('whenever an answer will be quoted, cited, or acted on'), when not to use it ('prefer ask_pipeworx for casual lookups'), the cost tradeoff (one extra LLM call), and names the alternative tool directly. This is exemplary usage 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

A3.8/5.0
Disambiguation4/5

Most tools target distinct purposes (e.g., ask_pipeworx vs. get_repo vs. validate_claim), but there is some overlap between ask_pipeworx and ask_pipeworx_grounded, and between bet_research and polymarket_edges. Overall, an agent can generally distinguish them.

Naming Consistency2/5

Tool names lack a consistent pattern: some are verb_noun (search_repos, get_user), others are noun_verb (entity_profile), and many are compound descriptor phrases (polymarket_arbitrage, scan_dependency). This mixed convention makes the set feel disjointed.

Tool Count2/5

38 tools is excessive for a server named 'Github', especially since many tools (e.g., ai_visibility_check, bet_research) are unrelated to GitHub functionality. The count would be appropriate for a broader 'Pipeworx' server but not for a focused GitHub server.

Completeness2/5

The GitHub-relevant tools are limited to read-only operations (get_repo, list_commits, etc.), lacking essential actions like creating/updating repos, issues, or pull requests. The inclusion of numerous non-GitHub tools does not compensate for these gaps.

Resources