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Glama

Openrouteservice

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

A4.6/5.0
Behavior5/5

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

Discloses technical details beyond annotations: BGE-base-en embeddings, cosine similarity, 500-char windows, 200K char cap with truncation, and character offsets.

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?

Two sentences that are front-loaded and contain no fluff; every sentence adds value.

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?

Covers input, behavior, and output hints (passages with offsets and scores); could mention output format but still adequate for a tool without output schema.

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%, but description adds context like max character count and natural-language query examples, plus underlying mechanics not in 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?

Clearly states it performs semantic search inside a fetched record using specific verb and resource, and distinguishes from siblings by mentioning pairing with 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 Guidelines4/5

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

Explicitly says when to use (record too big for prompt) and pairs with a sibling tool, but doesn't list alternative tools or when not to use.

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

B3.2/5.0
Disambiguation1/5

The tool set is a chaotic mix of geographic routing, AI visibility, betting analysis, memory storage, and random utilities. Many tools overlap in purpose (e.g., multiple data lookup tools like ask_pipeworx, discover_tools, resolve_entity), and the domain is completely inconsistent, making it nearly impossible for an agent to distinguish which tool to use for a given task.

Naming Consistency1/5

Tool names follow no consistent pattern; they mix snake_case (ai_visibility_check, ask_pipeworx), camelCase (generate_llms_txt), and arbitrary verbs without a clear verb_noun structure. Some names are vague (processV2-like patterns are absent, but e.g., 'forget' is a single verb). This chaotic naming prevents an agent from predicting tool functions.

Tool Count1/5

With 27 tools covering routing, AI marketing, betting, memory, and more, the count is extremely mismatched for the server's implied purpose ('Openrouteservice'). Even ignoring the name, the number is high and the scope is far too broad, making the set unwieldy and unfocused.

Completeness1/5

No coherent domain can be inferred from the tool set; it is an arbitrary collection. The routing tools are present but overshadowed by unrelated tools. For any single domain (e.g., betting or routing), the surface is either incomplete or includes extraneous tools, leaving the set severely lacking a clear purpose.

Resources