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Glama

Jpl Ssd

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. Description adds technical details (BGE-base-en, cosine, 500-char windows, 200K char cap with truncation) and notes that passages include offsets for verification.

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?

Four sentences, all essential. Purpose stated first, then usage guidance, then technical details. 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?

For a 3-param tool with no output schema, the description explains the return format (top-N passages, offsets, similarity scores) and covers edge cases (truncation). No gaps.

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?

100% schema coverage means parameters already documented well. Description adds real-world usage examples for 'text' and 'query', and mentions the truncation behavior for 'text'.

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, using specific examples (SEC filing, article). It 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 to use when the record is too large for the prompt and pairs with ask_pipeworx_grounded. Does not explicitly list when not to use, but context is clear.

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

Multiple tools overlap in purpose, especially the ask_pipeworx variants and the polymarket_* tools. An agent would struggle to distinguish between similar functions, increasing the risk of selecting the wrong tool.

Naming Consistency2/5

Tool names use inconsistent conventions: some are snake_case (close_approaches), some are verb_noun (generate_llms_txt), and others mix styles (ask_pipeworx vs. deep_research). No clear pattern is maintained across the set.

Tool Count1/5

At 35 tools, the server is bloated and unfocused. The majority of tools are unrelated to the 'Jpl Ssd' domain, which only has 4 relevant tools. The count is far too high for the stated purpose.

Completeness2/5

The JPL SSD coverage is minimal (only 4 tools), missing key functionalities like detailed object queries or bulk downloads. The remaining tools cover unrelated domains, so the surface is both incomplete for its primary domain and cluttered with off-topic tools.

Resources