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Glama

Pkg Go Dev

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, idempotent read operation. Description adds technical details: BGE-base-en embeddings, cosine similarity, 500-char windows, 200K char cap with truncation flag, and return of character offsets and similarity scores.

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?

Single, well-structured paragraph with purpose first, then usage, then technical details. Every sentence adds value, no redundancy.

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?

No output schema, but description explains return format (passages with offsets and similarity scores). Also covers input limits and truncation behavior. Sufficient for an intelligent agent to use 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% but description adds meaningful context: default and range for limit, example queries for query, and max size for text. Provides more than schema alone.

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?

Description clearly states the tool performs semantic search inside a fetched record. It provides concrete examples (SEC 10-K, article) and distinguishes itself from sibling tools like 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?

Describes when to use (record too big for prompt) and what to pass (text + query). Mentions pairing with ask_pipeworx_grounded, but does not explicitly state 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

B3.4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all performing similar data lookups. Additionally, many tools focus on prediction markets and arbitrage, which are unrelated to the server's implied Go development domain, causing confusion.

Naming Consistency2/5

Tool names follow inconsistent conventions: some use snake_case (e.g., 'latest_version', 'list_versions'), some use underscores in longer names (e.g., 'ai_visibility_check', 'ask_pipeworx_grounded'), and some use colons in descriptions but not in names. This mix reduces predictability.

Tool Count2/5

With 35 tools, the count is high for a server named 'Pkg Go Dev' that only offers a few Go module-related tools (e.g., get_go_mod, list_versions). The majority are unrelated Pipeworx tools, making the scope mismatched and excessive.

Completeness1/5

The tool set severely lacks coverage for Go development tasks. It only includes basic module lookup tools (version listing, mod retrieval) but misses essential operations like dependency analysis, build commands, or testing. The vast majority of tools cover unrelated domains (prediction markets, company profiles, etc.), leaving the server incomplete for its intended purpose.

Resources