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Bitfinex

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,798 across 1517 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

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 mark it read-only, idempotent, and non-destructive, and the description adds substantial behavior beyond that: exact success and refusal response shapes, refusal reasons, evidence extraction, and the extra LLM call cost. No contradiction exists between annotations and description.

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 dense but well structured: purpose is front-loaded, behavior and return shape follow, then usage guidance and cost tradeoff. Every clause adds decision-relevant information with no filler.

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 single-required-parameter tool with no output schema, the description is complete: it covers return envelope, refusal cases, when to use, when not to use, and cost implications. The schema covers the input, and the description covers everything else an agent needs to invoke it 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%, and the schema already documents the required question field and all five aliases. The description adds no extra parameter-level meaning, which is acceptable because the schema carries the full burden.

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 gives a specific verb and resource: it extracts a grounded answer using only tool-returned data, and explicitly contrasts itself with ask_pipeworx. This makes its purpose and distinction from a key sibling clear without needing to inspect schemas.

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?

The description explicitly states when to use it: whenever an answer will be quoted, cited, or acted on and facts must not be invented. It also states the alternative: prefer ask_pipeworx for casual lookups, plus the cost tradeoff.

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

C2.8/5.0
Disambiguation2/5

Many tools overlap or are near-duplicates: ask_pipeworx and ask_pipeworx_beta are currently identical, and there are multiple prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) with fine-grained but confusing distinctions. The mix of Bitfinex market data tools with an unrelated Pipeworx research suite makes tool selection genuinely ambiguous.

Naming Consistency2/5

All names use lowercase snake_case, but the semantic pattern is inconsistent: bare nouns (ticker, candles, trades, stats), verb_noun phrases (validate_claim, compare_entities, generate_llms_txt), and large prefixed families (ask_pipeworx*, polymarket_*) coexist. This mixed convention gives no reliable cue to a tool's function.

Tool Count2/5

42 tools is excessive for a server named 'Bitfinex'. Only about a dozen tools actually relate to the crypto exchange (ticker, candles, trades, book, liquidations, etc.); the rest are a grab bag of Pipeworx research, prediction markets, memory, and subscription features. The count bloats the surface and obscures the server's purpose.

Completeness2/5

The set has no coherent scope. For a Bitfinex server, there are no account/trading tools, only market data. For the buried Pipeworx functionality, the surface is extensive but unrelated to Bitfinex. The overall result is an incomplete hodgepodge with no clear lifecycle or workflow for a single domain.

Resources