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Glama

Textstats

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

A4.7/5.0
Behavior5/5

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

Annotations already mark it read-only, open-world, and idempotent, but the description adds genuinely novel behavioral detail: it costs an extra LLM call, returns an explicit refusal with enumerated reasons, and grounds the answer strictly in tool results with verbatim evidence. This goes well beyond the structured 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?

Every sentence earns its place: purpose, mechanism, return contract, refusal reasons, use cases, cost, and alternative. The description is dense but well-ordered and free of filler.

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 provides the full return shape and refusal reason list. It also explains the extra cost, names the preferred sibling for casual use, and gives explicit high-stakes use cases. Nothing needed to call it correctly 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 the question parameter is fully documented with aliases. The tool description does not add parameter-specific detail, but as the schema carries the full burden, 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?

States a specific purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It explicitly contrasts with the sibling ask_pipeworx ('Same routing ... then EXTRACTS the answer'), making the grounded vs. casual distinction unambiguous.

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?

Explicit use conditions are given: 'Use whenever an answer will be quoted, cited, or acted on...' with concrete examples like financial verdicts and legal claims. It also names the alternative: 'prefer ask_pipeworx for casual lookups' and gives a cost-based reason for choosing.

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
Disambiguation3/5

Many tools have overlapping purposes, e.g., multiple ask_pipeworx variants, deep_research, and bet_research all serve data retrieval with subtle differences. The descriptions help distinguish them, but the sheer number of similar tools creates ambiguity.

Naming Consistency4/5

Tool names are mostly snake_case with a verb_noun pattern (e.g., resolve_entity, validate_claim). A few are nouns like 'readability' or 'text_stats', but the overall style is consistent and readable.

Tool Count2/5

With 33 tools, the server is overstuffed. The server name 'Textstats' suggests a narrow focus, but it covers diverse domains (Polymarket, SEC, memory, subscriptions), making it feel bloated and unfocused.

Completeness3/5

The tool set covers a wide range of data sources and actions, but there are notable gaps for a 'text stats' server—only two tools directly handle text analysis. Additionally, obvious operations like a simple stock quote tool are missing, relying on ask_pipeworx instead.

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