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Glama

Nps

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

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

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Adds significant context beyond annotations: truncation at 200K chars, embedding model (BGE-base-en), window size (500-char overlapping), cosine similarity, and offset inclusion. Annotations already declare safety, but description enriches behavioral transparency.

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?

Description is concise: two paragraphs, first stating purpose and usage, second technical details. No fluff, every sentence adds information.

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?

For a tool with 3 params and no output schema, it covers everything: what the tool does, when to use, parameters usage, and expected output (passages with offsets and scores). Truncation and model details are also provided.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by providing context for 'text' (document text), 'query' (with examples), and 'limit' (default 5, range 1-20). This exceeds the bare 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 clearly states the action ('Semantic search INSIDE a fetched record'), specifies the resource (a long text like SEC 10-K), and distinguishes from siblings by naming ask_pipeworx_grounded as a complement.

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?

Explicitly states when to use ('when the record is too big to cram into the prompt') and provides a clear pairing with ask_pipeworx_grounded for grounding. No exclusions needed.

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
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., NPS park tools vs. Pipeworx queries vs. Polymarket analysis). Some overlap exists between ask_pipeworx and ask_pipeworx_grounded, and among the many Polymarket tools, but descriptions help differentiate them.

Naming Consistency3/5

Naming conventions vary: some use verb_noun (list_parks), others use descriptive phrases (ask_pipeworx_grounded, scan_competitor_ai_presence). While all use snake_case, there is no consistent pattern in prefixes or verb choice.

Tool Count2/5

35 tools is too many for a coherent server. The set appears to bundle multiple unrelated domains (NPS, Pipeworx, Polymarket, memory, subscriptions) into a single server, lacking focused scope.

Completeness3/5

Each domain has notable gaps (e.g., NPS lacks real-time conditions; Pipeworx lacks data ingestion tools; Polymarket lacks order placement). Some sub-areas (memory, subscriptions) are complete, but overall the surface is incomplete for the broad range of domains.

Resources