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Paleobiodb

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 5,798 across 1517 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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare read-only and idempotent; the description adds genuine behavioral detail: it uses only the tool result, returns verbatim evidence, can explicitly refuse with enumerated reasons, and costs an extra LLM call. No contradiction with the annotations.

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 purpose and usage are front-loaded and most sentences earn their place. The dense single paragraph is acceptable given the tool's complexity, though the precise '5,798 across 1517 sources' count is optional detail.

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?

With no output schema, the description supplies the full return shape and all refusal reasons, and it explains both the use case and the casual-lookup alternative. The input side is fully covered by the schema, so nothing needed for correct invocation is missing.

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% and every parameter is an alias for 'question', so the schema already carries the parameter meaning. The description adds no field-level detail, but with full coverage the baseline 3 is appropriate.

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 anchors the tool as a 'hallucination-resistant answer mode' for high-stakes reads and states that it extracts answers only from tool output, which clearly distinguishes it from ask_pipeworx and other siblings. It also names the sibling variant explicitly, so an agent can tell them apart without inferring.

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?

It gives explicit when-to-use guidance ('quoted, cited, or acted on', 'must not invent facts') and when-not-to-use ('prefer ask_pipeworx for casual lookups'), plus the cost tradeoff. This is unusually actionable guidance for a tool with several similar siblings.

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

A3.6/5.0
Disambiguation2/5

The tool set mixes general-purpose Pipeworx tools (ask_pipeworx, ai_visibility_check, bet_research) with only three paleontology-specific tools (find_fossils, get_taxon, list_subtaxa). Multiple similar 'ask_pipeworx' variants further blur distinctions, making it difficult for an agent to select the right tool without deep domain knowledge.

Naming Consistency3/5

Tool names are mostly in snake_case and somewhat descriptive, but the naming conventions vary widely: imperative verbs (find_fossils), interrogative (suggest_questions), and nouns (recent_alerts). The presence of multiple 'ask_pipeworx' variants with inconsistent suffixes (beta, grounded) adds confusion.

Tool Count2/5

With 34 tools, the server is oversized for its claimed paleontology focus. The vast majority of tools are unrelated to Paleobiology, making the server feel more like a general-purpose data API than a specialized paleontology tool. A focused server should have a smaller, targeted set.

Completeness2/5

For the Paleobiology Database purpose, the coverage is severely lacking: only three tools are directly relevant (find_fossils, get_taxon, list_subtaxa). Missing essential operations like searching taxa by name, retrieving occurrences by location, or accessing collections data. The tool set is not a coherent interface for the domain.

Resources