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Glama

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

A4.9/5.0
Behavior5/5

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

Annotations already mark it as read-only, idempotent, non-destructive. The description goes beyond by revealing implementation details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and character offsets for verification. This adds meaningful behavioral context not present in annotations.

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 front-loaded with the core purpose, then expands with examples, usage guidance, and technical details. Every sentence earns its place, and the flow from what/why/how is logical. No filler or repetition.

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?

Without an output schema, the description explains the return format (top-N passages, offsets, similarity scores). It covers input examples, limits, truncation behavior, and a sibling tool for grounding. For a tool with this complexity, it is fully self-contained and addresses both functional and edge-case aspects.

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%, so baseline is 3. The description adds value by giving examples for query ('supply-chain risk'), clarifying text type ('long tool result'), and explicitly stating the truncation flag for text over 200K chars. These details enhance understanding beyond schema descriptions.

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, with a specific verb and resource. It distinguishes itself from siblings by name-dropping ask_pipeworx_grounded and describing the workflow (fetch then search within). Examples like 'SEC 10-K body' make the purpose concrete.

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: 'Use when the record is too big to cram into the prompt'. It also provides an alternative workflow with ask_pipeworx_grounded, telling the agent to ground over passages rather than the whole document. This is clear, actionable guidance.

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

A3.8/5.0
Disambiguation2/5

Many tools overlap in purpose: three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are nearly identical routers, five polymarket_* tools cover similar prediction-market ground, and the server combines a tiny Warframe component with a broad Pipeworx/Polymarket toolkit, making selection ambiguous. The server name 'Warframe' does not match the majority of tools, further muddying intent.

Naming Consistency3/5

There is a loose snake_case convention, and several tools follow a verb_noun pattern (get_fissures, list_subscriptions, search_items, validate_claim). However, single-word imperative tools (remember, recall, forget), noun-phrase names (entity_profile, world_state, deep_research), and the polymarket_* family using inconsistent second words break the pattern. It is readable but not cleanly predictable.

Tool Count2/5

35 tools is far too many for a Warframe-focused server, and only 4 tools (get_fissures, get_invasions, search_items, world_state) actually relate to Warframe. The remaining 31 tools are a sprawling generic data/research/prediction-market collection that belongs to a different product, making the set bloated and unfocused for its apparent purpose.

Completeness3/5

Relative to the Warframe domain, the coverage is thin: only fissures, invasions, item search, and a combined world_state are offered, with obvious gaps like alerts, news, Nightwave, and event tracking. The broader Pipeworx/Polymarket toolset is internally comprehensive, but those tools do not serve the server's stated name and leave the Warframe scope incomplete.