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Glama

Hurricanes

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. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover safety traits (readOnly, openWorld, idempotent). Description adds specific technical details: BGE-base-en embeddings, 500-char windows, 200K char cap with truncation flag. Does not contradict 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?

Description is somewhat long but every sentence adds value, with key information front-loaded. Could be slightly trimmed, but effective.

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 rich annotations and schema, the description fully covers behavior, limits, and usage context. No output schema needed for this simple search tool.

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 has 100% coverage, so baseline 3 is appropriate. Description adds example queries and char limit clarification, but schema already handles parameter documentation adequately.

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 it performs semantic search inside a fetched record, with examples like SEC 10-K body. It distinguishes from siblings (e.g., ask_pipeworx_grounded) by explaining its role in the workflow.

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 ('record too big to cram into the prompt') and notes it pairs with ask_pipeworx_grounded. Provides clear context and alternatives.

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

All Hurricanes toolsInspect
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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes with clear descriptions, but ask_pipeworx_beta is nearly identical to ask_pipeworx, and the presence of several meta-tools may cause slight confusion. Overall, an agent can differentiate most tools.

Naming Consistency4/5

Tool names consistently use lowercase_with_underscores and follow an action_domain pattern (e.g., validate_claim, scan_competitor_ai_presence). There is a mix of verb-noun and noun-verb, but the pattern is predictable and readable.

Tool Count3/5

33 tools is on the higher end, but given the broad scope (finance, drugs, prediction markets, data retrieval, memory), the count is reasonable. However, the server name 'Hurricanes' suggests a narrower focus, making the count feel excessive for that domain.

Completeness4/5

The tool set covers a wide range of data domains and includes meta-tools for discovery, grounded answers, and subscriptions. Minor gaps exist (e.g., no non-US company data), but overall the surface is comprehensive for a general-purpose data server.

Resources