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Pkg Go Dev

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?

Discloses rich behavioral detail beyond the readOnly/openWorld/idempotent annotations: the precise success return shape with evidence and confidence, the explicit refusal reasons enum, and the grounding constraint that the answer must come only from the tool result. No contradiction with 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 differentiator ('Hallucination-resistant answer mode for high-stakes reads') and every subsequent sentence adds concrete value: routing, extraction behavior, return/refusal formats, usage guidance, and cost trade-off. No filler or redundancy.

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?

Despite lacking an output schema, the description fully specifies the success and refusal response structures, enumerates refusal reasons, explains routing and grounding behavior, and gives clear usage boundaries. An agent has everything it needs to decide when to invoke this tool and what to expect.

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%, so the baseline is 3. The description adds no additional parameter-level meaning beyond what the schema already provides for the 'question' parameter and its aliases. It 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 clearly identifies this as a hallucination-resistant, grounded answer mode that extracts answers exclusively from tool results. It differentiates itself from ask_pipeworx by the grounded extraction and evidence requirement, making the purpose and distinction unambiguous.

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 this tool (answers that will be quoted, cited, or acted on; situations where facts must not be invented) and when to prefer the alternative (casual lookups), while naming ask_pipeworx as the sibling with the same routing. This gives an agent clear routing logic.

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

B3.4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all performing similar data lookups. Additionally, many tools focus on prediction markets and arbitrage, which are unrelated to the server's implied Go development domain, causing confusion.

Naming Consistency2/5

Tool names follow inconsistent conventions: some use snake_case (e.g., 'latest_version', 'list_versions'), some use underscores in longer names (e.g., 'ai_visibility_check', 'ask_pipeworx_grounded'), and some use colons in descriptions but not in names. This mix reduces predictability.

Tool Count2/5

With 35 tools, the count is high for a server named 'Pkg Go Dev' that only offers a few Go module-related tools (e.g., get_go_mod, list_versions). The majority are unrelated Pipeworx tools, making the scope mismatched and excessive.

Completeness1/5

The tool set severely lacks coverage for Go development tasks. It only includes basic module lookup tools (version listing, mod retrieval) but misses essential operations like dependency analysis, build commands, or testing. The vast majority of tools cover unrelated domains (prediction markets, company profiles, etc.), leaving the server incomplete for its intended purpose.

Resources