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Glama

Open Elevation

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 indicate safe read-only operation. Description adds valuable behavioral details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char limit with truncation flag, idempotent. No contradiction.

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?

Six sentences, front-loaded with core purpose, then usage guidance, then technical details. Every sentence adds value; no fluff.

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?

Despite no output schema, description explains return format (passages with offsets and similarity scores), truncation behavior, and integration hint. Covers input, output, limitations, and pairing.

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 examples for query parameter and specifies default (5) and range (1-20) for limit, going beyond schema descriptions.

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 semantic search inside a fetched record, gives concrete examples (SEC 10-K, article), and distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and avoiding cramming full text.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: when record is too large for prompt. Mentions return format (passages with offsets) and pairing with another tool. No explicit 'when not to use', but context implies alternative is direct inclusion.

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

B3.3/5.0
Disambiguation1/5

The tool set covers many unrelated domains (elevation, finance, prediction markets, AI visibility, etc.) with multiple overlapping tools per domain (e.g., three 'ask_pipeworx' variants, several 'polymarket' tools). An agent would struggle to distinguish which tool to use for a given task.

Naming Consistency2/5

Names follow no consistent pattern: some use snake_case with vague verbs (e.g., 'process', 'run'), others use descriptive but unrelated prefixes ('ai_', 'ask_pipeworx_', 'polymarket_'). There is no uniform verb_noun structure.

Tool Count3/5

With 32 tools, the count is reasonable for a large server, but the vast majority are irrelevant to the server's stated purpose (elevation). This mismatch makes the count inappropriate.

Completeness1/5

The server name 'Open Elevation' implies a focus on elevation data, yet only 2 of 32 tools (get_elevation, get_elevations) are related. There are severe gaps: no area elevation, no geocoding, no terrain analysis. The tool surface is largely off-topic.

Resources