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Google_maps

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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds specific behavioral details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation and flagging. This fully discloses operational behavior.

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?

The description is a single, well-structured paragraph with no wasted words. Each sentence contributes essential information, and the key points (purpose, usage, behavior) are front-loaded.

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, the description adequately explains return values (passages, offsets, scores). All 3 parameters are covered in schema and description. The tool's behavior is fully described, making it complete for an agent to use.

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% with descriptions. The description adds value by explaining the intended use of 'text' (fetched record), providing query examples, and stating default and max for limit. While schema is already good, the description enhances it with practical guidance.

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 the tool performs semantic search inside a fetched record, specifying input (text and query) and output (top-N passages with offsets and scores). It distinguishes from siblings like ask_pipeworx_grounded by noting they pair together.

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 to use when the record is too large for the prompt, and how it pairs with ask_pipeworx_grounded for grounding. Provides clear context on when it is beneficial, effectively guiding agent decision-making.

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

A3.5/5.0
Disambiguation2/5

The tool set mixes unrelated domains (maps, prediction markets, npm dependencies, memory storage) under one server named 'Google_maps'. While individual tool descriptions are clear, an agent cannot easily distinguish which tools belong to the maps domain and which are extraneous, causing confusion about the server's actual purpose.

Naming Consistency2/5

Tool names lack a consistent convention. Maps tools use 'maps_' prefix, but other tools have names like 'ask_pipeworx', 'bet_research', 'forget', etc., mixing prefixes, verb styles, and underscore usage. No unified naming pattern across the set.

Tool Count2/5

37 tools is excessive for a specialized maps server. Only 7 tools are map-related; the remaining 30 cover disparate domains (financial data, prediction markets, system utilities), making the server seem like a random collection rather than a focused integration.

Completeness2/5

For a maps server, common operations like static map generation, place photos, or timezone lookups are missing. The inclusion of many non-maps tools creates a 'kitchen sink' effect, undermining completeness for the stated purpose. The tool surface is severely incomplete if judged by the server name.

Resources