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Opentreeoflife

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 5,738 across 1499 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

A4.7/5.0
Behavior5/5

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

The annotations already signal read-only, idempotent, non-destructive behavior, and the description goes well beyond them. It discloses the refusal contract with exact refusal_reason values, the evidence-as-verbatim-quote behavior, the success response shape, and the extra LLM-call cost — all behavior an agent needs beyond annotations.

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 dense but every sentence earns its place: purpose, routing, grounding mechanism, return and refusal shapes, usage conditions, and cost trade-off. It is front-loaded with the most decision-relevant fact and ends with the alternative routing guidance.

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 fully compensates by specifying both the success response fields and the full refusal_reason enumeration. It also names the sibling it routes like and explains the cost difference, so an agent can decide correctly in context.

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 even without additional parameter explanation. The description correctly says 'question in natural language' is the input and does not need to repeat alias details already documented in the schema.

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 uses a specific verb and resource: a hallucination-resistant answer mode that routes through the same machinery as ask_pipeworx, fetches data, and extracts an answer only from the tool result. It clearly distinguishes itself from the sibling ask_pipeworx by grounding and refusal behavior.

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 explicitly states when to use the tool (answers that will be quoted, cited, or acted on, and where invention is unacceptable) and when not to (casual lookups → prefer ask_pipeworx), citing the extra LLM call cost. This is ideal routing guidance with no ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Opentreeoflife toolsInspect
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TDQS

A3.7/5.0
Disambiguation3/5

Many tools have clear distinct purposes, but ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research have overlapping functionality with subtle differences, causing potential confusion. Overall, most tools are distinguishable.

Naming Consistency2/5

Tool names use a mix of patterns: some follow verb_noun (validate_claim, resolve_entity), others are compound nouns (polymarket_arbitrage, ai_visibility_check), and some are plain verbs (recall, forget). Inconsistent style and length reduce predictability.

Tool Count3/5

At 34 tools, the count is on the high side for a typical server, but it might be manageable if the scope were broad. However, the server name 'Opentreeoflife' suggests a narrow biological focus, making the large count feel mismatched and excessive.

Completeness1/5

For a server named after a tree-of-life database, only three tools (match_names, common_ancestor, taxon_info) are relevant. Missing fundamental operations like listing children, searching taxa, or retrieving phylogenetic trees leaves the domain severely incomplete.

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