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Mitre Cwe

by io.github.pipeworx-io

Deep Research

deep_research
Read-onlyIdempotent

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1499 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,738 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a hop field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan).
questionYesThe research question, in natural language. Broad/multi-part is fine — decomposition is the point.

TDQS

A4.8/5.0
Behavior5/5

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

Even though annotations already mark this read-only, idempotent, and non-destructive, the description adds substantial behavior beyond that: never invents answers, returns explicit gaps[], contradictions[] for standard/thorough, stable pipeworx:// citations, semantic excerpting, latency expectations, and second-hop recovery behavior. No contradiction with annotations is present.

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 long, but it is information-dense and every major block earns its place for a tool with this much behavioral nuance. It could still be tightened by trimming parenthetical statistics and some repetition of alternatives, so it is not a perfect 5.

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?

There is no output schema, so the description carries the full burden of explaining return values. It does so thoroughly: findings packet contents, citation format, fetchability guarantee, gaps[], contradictions[], hop field, latency, account requirements, and routing to alternatives. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining what the depth levels do in practice (gap recovery for standard, chasing leads for thorough, contradictions scan) and clarifying that the question parameter expects natural-language broad/multi-part phrasing.

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 the verb and resource: 'Grounded multi-source research across Pipeworx's 1497 STRUCTURED data sources... in ONE call.' It explicitly contrasts with open-web search and distinguishes itself from sibling ask_pipeworx by naming the alternative and the conditions for choosing it.

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: best for broad/multi-part questions, use ask_pipeworx for a single lookup, prefer ask_pipeworx for breaking/current-news topics, and use ask_pipeworx if not signed in. It also explains depth-level tradeoffs, leaving little ambiguity about selection.

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

Only 7 of 38 tools are specific to CWE (weakness, children, parents, etc.), while the rest are generic data query, prediction market, and utility tools. This creates massive overlap and confusion: an agent looking for CWE data will encounter many unrelated tools with similar generic names like ask_pipeworx, deep_research, etc.

Naming Consistency2/5

CWE-specific tools use a consistent noun pattern (weakness, children, parents, etc.), but the remaining tools mix snake_case, camelCase, and no clear pattern (e.g., ask_pipeworx, bet_research, generate_llms_txt). The overall naming is inconsistent.

Tool Count1/5

38 tools is far too many for a CWE server. The vast majority are unrelated to CWE, making the tool surface bloated and unfocused. A concise set of ~5-10 CWE-specific tools would be appropriate.

Completeness3/5

The CWE-specific tools (weakness, category, view, children, parents, descendants, relationship) cover the main use cases for querying the CWE database. However, the server also includes many unrelated tools that dilute its purpose, making it feel incomplete for someone strictly needing CWE data.

Resources