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Glama

Temperature Random

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, idempotent behavior. Description adds unique behavioral details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flagging, and passage offsets for verification. 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?

Three sentences, each serving a distinct purpose: definition, usage scenario+benefits, and technical details. No redundant information, highly efficient.

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?

Given no output schema, the description covers return format (passages with offsets and scores), pruning behavior, partner tool, and technical constraints. Provides all necessary context for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (all params have schema descriptions). The description reinforces the schema but does not add significant new meaning beyond examples and default values. Baseline score of 3 is appropriate.

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 defines the tool as performing semantic search within a previously fetched record, with specific examples (SEC 10-K, article). It distinguishes from sibling tools by focusing on inner-document search and referencing ask_pipeworx_grounded as a partner.

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 states when to use: when records are too large for prompts. Also pairs with ask_pipeworx_grounded, providing complementary usage. No ambiguity about context.

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

A3.6/5.0
Disambiguation2/5

There are several overlapping clusters: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions through the same 5,743-tool catalog, and ask_pipeworx_beta is currently an exact duplicate of ask_pipeworx. The Polymarket opportunity tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) also have fuzzy boundaries despite detailed descriptions.

Naming Consistency2/5

There is no consistent naming pattern across the set: some tools are verb-first (ask_pipeworx, compare_entities, validate_claim), some are noun-first (entity_profile, recent_changes, polymarket_edges), and some are compound/multi-word oddities (temperature_random_generate, ai_visibility_check). Small internal clusters like remember/recall/forget and subscribe/unsubscribe/list_subscriptions show mini-consistency, but the overall convention is mixed and unpredictable.

Tool Count2/5

With 32 tools, the server is over-stuffed, and many of them serve the same broad Pipeworx data/research purpose while one unrelated temperature tool rides along. The count is above the 25-tool threshold where a set starts to feel unwieldy, and several tools could be merged or dropped without losing real capability.

Completeness4/5

Assuming the intended scope is the Pipeworx data/agent platform implied by 31 of the 32 tools, coverage is strong: lookups, grounded verification, deep research, entity profiles, comparisons, entity resolution, claim validation, tool discovery, memory, subscription lifecycle, prediction-market analysis, execution risk, and feedback are all represented. The temperature_random_generate tool is a domain misfit rather than a completeness gap, and there are few obvious missing operations for the stated workflows.

Resources