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Ietf Datatracker

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent, destructive=false), the description adds valuable behavioral context: it is 'derived from CF analytics-engine', contains 'no PII', and is 'cached 5min-1h depending on window'. These details inform the agent about data provenance, privacy, and freshness.

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 slightly longer than minimal but every sentence adds value: the opening sentence states the core function, then returns, use cases, and technical details. It is well-structured and front-loaded, though it could be trimmed slightly without losing content.

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 adequately explains the return values ('top tools, top packs, and total call volume', 'just (pack, tool, count)'). Combined with the good annotations and a single simple parameter, the description is complete for an agent to decide whether and how to invoke the tool.

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?

The input schema already fully describes the 'window' parameter, including its enum values and meaning (100% coverage). The description merely reiterates the window options without adding new semantics. Since schema coverage is high, a baseline score 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 states what the tool does: 'Returns the top tools, top packs, and total call volume over a recent window.' This is a specific verb, resource, and scope. It also distinguishes itself from siblings by focusing on what other AI agents are calling, which is a unique angle not seen in the sibling list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases: discovering hot data sources, confirming canonical tool choice, and checking alignment with agent needs. It gives clear context for when to use the tool, though it doesn't explicitly state alternatives or exclusions relative to sibling tools.

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

Several tools have overlapping or poorly distinguished purposes. For instance, `ask_pipeworx` and `ask_pipeworx_beta` have nearly identical descriptions, and `ask_pipeworx_grounded` also shares the same routing but adds a different output format. The `ai_visibility_check` and `scan_competitor_ai_presence` tools also overlap significantly.

Naming Consistency3/5

There is some consistency with verb_noun patterns (e.g., `resolve_entity`, `search_within`, `subscribe`, `unsubscribe`). However, there are many deviations: `ask_pipeworx`, `pipeworx_feedback`, `pipeworx_trending`, `entity_profile`, `scan_dependency`, and `polymarket_edges` break the pattern, mixing descriptive names with non-standard prefixes.

Tool Count4/5

37 tools is slightly above the ideal range for a single MCP server, but the tools cover a very broad and varied domain (IETF data, company research, prediction markets, package scanning, memory, etc.). The count is high but still within a manageable scope for a multi-purpose utility server.

Completeness2/5

The server combines tools from two very different domains: IETF Datatracker (document/WG/person lookups) and Pipeworx (data retrieval, prediction markets, company analysis). The IETF-related tools are sparse and incomplete (only document search, document, person, wg, wgs_search, rfc are present—no ability to create or modify records). The Pipeworx side is extensive but leaves notable gaps (e.g., no tool for submitting comments or editing IETF documents).

Resources