Skip to main content
Glama

Weather Gc Ca

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".

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description goes beyond by detailing embeddings (BGE-base-en), chunking (500-char overlapping windows), and limits (200K chars with truncation flag). No contradiction with 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 serves a purpose: core function, use case, examples, pairing, technical details. No filler. Front-loaded with the main action.

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 no output schema, the description explains return values (top-N passages, offsets, scores), covers limits and pairing, and provides sufficient context for an agent to decide when to use. Complete for the complexity.

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 explaining the role of each parameter: 'text' is already fetched, 'query' is natural language with examples, 'limit' with default. Also reiterates the text cap. Adds meaning beyond 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 it performs semantic search inside a fetched record, with specific examples (SEC 10-K, article) and distinguishes itself from siblings like ask_pipeworx_grounded. Verb+resource is explicit: 'search inside a source'.

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 tells when to use ('when the record is too big to cram into the prompt'), mentions pairing with ask_pipeworx_grounded, and explains the output (passages with offsets) enabling verification. No ambiguity.

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

All Weather Gc Ca 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

A3.8/5.0
Disambiguation3/5

Many tools have distinct purposes (e.g., current_observations vs. climate_daily), but several query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, bet_research) overlap in function, all routing questions to a large tool catalog. This can confuse an agent about which to use.

Naming Consistency3/5

Names are snake_case but follow no consistent pattern: some are verb_noun (ask_pipeworx), some noun_adjective (climate_daily), others compound (ai_visibility_check). The mix is readable but not predictable.

Tool Count2/5

33 tools is high for a server named 'Weather Gc Ca', which implies a focused weather service. Many tools are unrelated to weather (Polymarket, SEC, FDA, etc.), making the count inflated and mismatched to the server's apparent scope.

Completeness2/5

For weather, the server covers alerts, current observations, and climate records but lacks forecasts, radar, satellite imagery, and station listings. While the broader Pipeworx catalog is extensive, the weather-specific surface is incomplete for a dedicated weather tool.

Resources