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Glama

Usgs Earthquakes

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

A4.8/5.0
Behavior5/5

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

Even though annotations already provide readOnly/openWorld/idempotent hints, the description adds significant behavioral detail: embedding model (BGE-base-en), cosine similarity over 500-char overlapping windows, a 200K char cap with truncation flag, and the return of character offsets and similarity scores. It also mentions the ability to verify verbatim quotes—context 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?

The description is slightly long but every sentence carries distinct information: purpose, use case, pairing, technical mechanism, and limits. It is front-loaded with the core purpose in the first sentence and avoids redundant phrasing. Would earn 5 if slightly trimmed of minor technical details that could be inferred.

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 retrieval tool with no output schema, the description explains the return format (passages with offsets and similarity scores), states input cap and truncation behavior, and covers the main use case. It also contextualizes the tool within the sibling ecosystem (ask_pipeworx_grounded) and explains the algorithm. This is complete enough for an agent to decide and invoke correctly.

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 semantic value by giving concrete examples for the 'text' param (SEC 10-K body, article, long tool result) and example queries for 'query' (supply-chain risk, fiscal year revenue). It also clarifies the 'limit' param's role implicitly via 'top-N passages' and the default. This goes beyond mere schema labels.

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 opens with 'Semantic search INSIDE a fetched record,' which names the specific verb, resource, and scope. It clearly distinguishes itself from sibling tools by contrasting with ask_pipeworx_grounded and emphasizing it operates on already-fetched text rather than fetching or grounding over whole documents.

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 notes it 'saves context.' It also names the alternative approach (ask_pipeworx_grounded) and explains the intended pairing ('fetch with the gateway, ground over the relevant passages'), giving concrete usage context.

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
Disambiguation1/5

The tool set is dominated by tools unrelated to USGS earthquakes (e.g., Polymarket betting, company profiles, memory operations). An agent would find it nearly impossible to distinguish the few earthquake-specific tools from the multitude of unrelated ones, leading to severe misselection.

Naming Consistency3/5

Most tool names follow a verb_noun pattern with underscores (e.g., search_earthquakes, count_earthquakes), which is consistent. However, the variety of verbs and domains creates a sense of incoherence, and some tool names are overly generic (e.g., process, run) in the broader context, though those are not present here. The naming pattern is acceptable but the inconsistency in domain scope reduces clarity.

Tool Count1/5

With 29 tools but only 3 directly related to earthquakes, the tool count is grossly inappropriate. The server's name suggests a focused purpose, but the vast majority of tools belong to other domains (e.g., Pipeworx queries, Polymarket betting, company data). This extreme mismatch makes the tool set bloated and misleading.

Completeness2/5

For earthquake data, the server provides only search, count, and get by ID. Missing are common operations like listing recent quakes, subscribing to alerts, or updating/correcting data. The coverage is minimal and insufficient for a comprehensive earthquake tool server, leaving significant gaps that agents could not work around.

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