Skip to main content
Glama

Pirate Weather

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

A4.7/5.0
Behavior5/5

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

Annotations already declare safety (readOnly, idempotent, non-destructive). Description adds details about truncation at 200K chars with flag, embedding model (BGE-base-en), window size (500-char overlapping), and cosine similarity, going well 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is two well-structured sentences that front-load the core action. While packed with details, every sentence serves a purpose; slight room for tighter phrasing but no waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given only 3 parameters and no output schema, description explains return format (passages with offsets and similarity scores), constraints (200K chars truncation), and embedding details, providing sufficient context for an agent to use the tool effectively.

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. Description adds value by specifying the 200K char limit for 'text', default of 5 for 'limit', and providing natural-language query examples for 'query', enhancing understanding.

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, specifies the use case (records too large for prompt), and differentiates from sibling tools like ask_pipeworx_grounded.

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 says when to use ('when the record is too big to cram into the prompt') and suggests an alternative workflow ('Pairs with ask_pipeworx_grounded'), providing clear guidance.

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

All Pirate Weather toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.1/5.0
Disambiguation3/5

Tools are mostly distinct in purpose but some overlaps exist, e.g., multiple ask_pipeworx variants and several polymarket analysis tools. Detailed descriptions help, but the sheer variety and similar intent of some tools could confuse agents.

Naming Consistency2/5

Naming is highly inconsistent: snake_case (ai_visibility_check), single words (forecast), action_noun (bet_research), prefixes (polymarket_, pipeworx_), and descriptive phrases (scan_competitor_ai_presence). No uniform pattern.

Tool Count2/5

33 tools is too many for a server named 'Pirate Weather' that only has two weather-specific tools. The tool count feels inflated with many meta-tools and unrelated domains, exceeding a coherent scope.

Completeness2/5

Coverage is incomplete for the implied weather focus (only two tools). Other domains like prediction markets are better covered, but overall the surface is a mix of partial offerings with clear gaps (e.g., no update/delete for subscriptions).

Resources