Skip to main content
Glama

Gemini Crypto

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral detail: embedding model (BGE-base-en), chunking (500-char overlapping windows), similarity measure (cosine), character cap (200K chars) with truncation flagging, and output features (offsets, similarity scores). No contradictions.

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 concise (4 sentences) and front-loaded with the core purpose. Every sentence adds unique value—usage guidance, technical details, and pairing advice. No redundancy or fluff.

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 the tool's moderate complexity, full schema coverage, rich annotations, and no output schema, the description fully covers what the agent needs: purpose, when to use, technical behavior, and output characteristics. It also mentions a related tool for chaining.

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 description coverage is 100% with adequate descriptions for all three parameters. The tool description adds some contextual examples for 'query' but does not substantially enhance meaning beyond the schema. Baseline 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 states it performs 'semantic search INSIDE a fetched record', with specific examples like SEC 10-K or article, and explicitly contrasts with siblings (e.g., ask_pipeworx_grounded). This provides a clear verb+resource and differentiates from other tools.

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 advises using when 'the record is too big to cram into the prompt' and describes pairing with ask_pipeworx_grounded, giving clear context and an alternative. This guides the agent precisely.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Gemini Crypto toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation3/5

Many tools serve overlapping research purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, entity_profile, compare_entities), which could confuse an agent. However, descriptions help differentiate them by use case (single vs multi-part, grounded vs standard, etc.). Some overlap remains.

Naming Consistency3/5

Tool names mix patterns: some are verb_noun (ask_pipeworx, bet_research), others are noun_phrase (price_feed, recent_alerts) or adjective_noun (ticker_v2). No strong naming convention, but all use snake_case consistently, making them readable.

Tool Count3/5

40 tools is on the high side for a single server, covering both Gemini exchange data and Pipeworx's broad knowledge tools. While each tool serves a purpose, the scope feels broad, and some tools could be separated into dedicated servers.

Completeness4/5

The Gemini exchange tools cover essential read-only data (order book, candles, ticker, trades), but lack order placement, likely intentionally. The Pipeworx tools provide extensive research capabilities across many domains, leaving few gaps for the stated purposes.

Resources