Skip to main content
Glama

Biosamples

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, and idempotent hints. The description goes further by explaining the exact success response shape, the refusal structure with specific refusal_reason values, and the fact that extraction uses ONLY tool result content. This fully discloses the behavioral contract beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average but every clause earns its place: purpose, routing mechanism, response schema, refusal reasons, use cases, and cost tradeoff are all relevant. The main purpose is front-loaded, though slightly dense.

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 though there is no output schema, the description fully explains what the tool returns on success and failure, including refusal_reason enums. It covers cost, routing, and when to prefer an alternative, making the tool self-sufficient for an agent.

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% — the schema already documents the single 'question' parameter and all aliases. The description does not add new parameter semantics beyond the schema, so 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?

States a specific verb ('ask'), a distinct mode ('grounded'), and a precise resource context (5,724 tools across 1,497 sources). It clearly differentiates from ask_pipeworx and ask_pipeworx_beta by emphasizing hallucination resistance and evidence-grounded answers.

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 (answers that will be quoted, cited, or acted on; high-stakes financial, legal, or medical claims) and when not to use (casual lookups). It even names the cheaper alternative, ask_pipeworx, and the cost tradeoff of one extra LLM call.

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

All Biosamples 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
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants (beta currently identical), and the set includes five-plus prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) with fuzzy boundaries. Long descriptions help, but an agent selecting among them would frequently struggle to pick the right one.

Naming Consistency3/5

Names are uniformly lowercase snake_case, but the pattern is inconsistent: verb_first names (search_samples, validate_claim, resolve_entity) mix with noun-style names (entity_profile, pipeworx_trending, polymarket_arbitrage) and bare imperatives (remember, recall, forget, subscribe). It is readable, but there is no predictable verb_noun convention across the set.

Tool Count2/5

33 tools is well over the coherent range, and the count is especially inflated because the server is named Biosamples yet only two tools (search_samples, get_sample) actually belong to that domain. The remaining 31 tools are an unrelated mix of Pipeworx research, prediction-market, memory, and subscription utilities, including redundant variants.

Completeness2/5

For the stated Biosamples purpose, only search and retrieve exist—no submission, update, or batch operations—so the domain surface is a read-only fragment. For the broader accidental scope of the other tools, the set is a grab bag with no coherent lifecycle, leaving significant gaps regardless of which domain is considered primary.

Resources