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Feodotracker

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 5,738 across 1499 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.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already signal read-only, open-world, and idempotent behavior, so the bar is lower, but the description adds substantial behavioral detail: refusal reasons, evidence verbatim quotes, data truncation handling, and the guarantee to only use tool results. No contradiction with annotations exists.

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 every sentence earns its place: purpose, routing behavior, extraction guarantee, output shape, refusal reasons, use cases, and cost tradeoff. The most critical distinction from the sibling is front-loaded in the first sentence.

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?

Even without an output schema, the description fully enumerates the success and failure return shapes, so an agent knows exactly what to expect. It also covers the alternative tool, the cost implication, and the safety-critical usage context — nothing needed to invoke correctly is missing.

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 all six parameters are already documented as aliases of 'question' in the input schema, so the description does not need to add parameter-level meaning. It correctly focuses on behavior rather than re-explaining the schema.

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 names a specific behavior — 'Hallucination-resistant answer mode' that 'EXTRACTS the answer using ONLY what the tool result contains' — and clearly distinguishes it from the sibling ask_pipeworx by emphasizing grounded extraction rather than plain retrieval. It also specifies the exact return contract, making its purpose unmistakable.

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?

It explicitly states when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'), and names the alternative directly. It even quantifies the tradeoff — one extra LLM call — giving the agent a concrete decision rule.

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

Multiple tools occupy the same conceptual space: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all answer 'what can this server do' or 'look this up' in overlapping ways. The Polymarket suite (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) and the memory trio (remember, recall, forget) also create boundary confusion despite long descriptions.

Naming Consistency2/5

Naming is a mixed bag: some tools are imperative verbs (check_ip, forget, remember, validate_claim), some are bare nouns or adjectives (list, recent, aggressive), and many are noun compounds (entity_profile, polymarket_edges, pipeworx_trending). No consistent verb_noun or domain-prefix pattern holds across the set.

Tool Count2/5

35 tools is excessive for a server ostensibly named Feodotracker, whose core blocklist surface is only four tools (list, recent, aggressive, check_ip). The rest is a sprawling collection of unrelated Pipeworx, Polymarket, memory, subscription, and utility features that would be better split into separate servers.

Completeness3/5

The core blocklist domain is minimally covered: you can list, filter by family/status, check an IP, and see recent additions, which covers basic read-only use. However, there are notable gaps and dead ends, such as no historical lookup beyond recent hours and no per-IP detail beyond membership, while the bundled Pipeworx/Polymarket features are thorough but make the overall surface feel scattershot.

Resources