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Dailymed

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 5,724 across 1497 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?

Discloses rich behavioral detail beyond annotations: it extracts answers only from tool results, may return explicit refusals, enumerates refusal reasons, and describes the success response shape. This provides far more transparency than the readOnlyHint/openWorldHint annotations alone.

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: core differentiator, routing behavior, return/refusal contract, use cases, and cost trade-off. It is front-loaded with the most important property and contains 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?

With no output schema, the description fully compensates by documenting the success response fields and refusal reason enum. Together with the single required parameter and clear usage guidance, nothing essential is missing for correct invocation.

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%, so the schema already documents the single semantic parameter and all aliases fully. The description adds no additional parameter-level meaning, but with full schema coverage the baseline of 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 identifies this as a hallucination-resistant answer mode for high-stakes reads, with a specific verb/resource and explicit contrast to ask_pipeworx. It distinguishes itself from the sibling tool by its grounded output contract, not just its title.

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?

Provides explicit when-to-use guidance ('whenever an answer will be quoted, cited, or acted on') and when-not-to-use ('prefer ask_pipeworx for casual lookups'), citing the cost difference. This gives an agent a clear decision rule between the two sibling tools.

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

All Dailymed toolsInspect
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TDQS

B3.4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; deep_research; bet_research and multiple polymarket tools). This makes it difficult for an agent to distinguish which tool to use.

Naming Consistency4/5

Tool names consistently use snake_case and follow a verb_noun or noun_verb pattern. However, some names are very long and descriptive (e.g., polymarket_kalshi_spread, scan_competitor_ai_presence), which is acceptable but slightly inconsistent in length.

Tool Count3/5

With 38 tools, the server is on the high side of reasonable. Many are meta-tools or query routers, which inflates the count. The scope is very broad, covering diverse domains, making the number somewhat justifiable but still feeling heavy.

Completeness3/5

For the Dailymed domain, tools cover label search, retrieval, history, and comparison. However, many other domains (e.g., finance, prediction markets) rely on a handful of routing tools (ask_pipeworx) rather than dedicated tools, leaving the coverage uneven and not fully self-contained.

Resources