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Glama

Outlook Contacts

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 declare readOnlyHint and idempotentHint, and the description adds substantial behavior beyond that: the extraction-only-from-tool-result guarantee, explicit refusal reasons, structured return shape, and one extra LLM call cost. 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, behavior and return contract, when to use, and trade-off versus the sibling. The most important qualifier, hallucination-resistant, is front-loaded. It is long because the tool's behavior is genuinely nuanced, not because of 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?

No output schema exists, so the description correctly documents the full success and refusal return shapes. It also covers routing behavior, safety properties through annotations, cost trade-offs, and explicit usage conditions. Everything an agent needs to select and invoke this tool correctly is present.

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 schema already documents the single meaningful parameter and aliases. The description does not add much parameter-specific detail, but it reinforces that the tool fills arguments from the question. Baseline 3 is appropriate because the structured schema carries the load.

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 states a specific verb+resource: a hallucination-resistant grounded answer mode for high-stakes reads. It explicitly differentiates itself from ask_pipeworx by noting it extracts answers only from tool results and includes a refusal pathway. An agent can distinguish it from siblings without opening any 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?

Provides explicit when-to-use guidance: use whenever the answer will be quoted, cited, or acted on, and facts must not be invented. It also names the alternative ask_pipeworx and explicitly says to prefer it for casual lookups due to the extra LLM call cost. This is model guidance.

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

Several tools are near-duplicates by name and function, notably ask_pipeworx vs ask_pipeworx_beta, and the dense Polymarket family (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread). Long descriptions help, but an agent cannot reliably pick between these without reading closely.

Naming Consistency4/5

Almost all names are lowercase_snake_case and mostly verb-leading (discover_tools, resolve_entity, unsubscribe), but there are noun-first exceptions (entity_profile, recent_changes, pipework_trending) and several phrasal or compound forms. This is a minor deviation from a clean verb_noun pattern rather than a chaotic mix.

Tool Count2/5

31 tools is over the heavy threshold, and the set feels bloated: multiple query routers, a half-dozen overlapping Polymarket tools, and two AI-visibility probes that could be merged. The count is especially hard to justify for a server named Tools 'OutLook Contacts', since none are contact management.

Completeness1/5

For the server's stated domain, Outlook Contacts, there are zero tools in that domain — no create contact, no list contacts, no update, no delete, no folders or mailboxes. Even though the actual Pipework toolkit is broad for its own domain, this set fails its declared intend domain entirely.

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