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TensorFeed

by ai.tensorfeed

check_ai_supply_chain_risk

Check the TensorFeed AI/MCP/LLM supply-chain IOC feed. Returns publicly-disclosed malicious npm/PyPI packages whose name or summary signals relevance to AI agent operators. With no args, returns the whole snapshot (typically a small number of entries). With "package_name", returns only entries matching that name (substring, case-insensitive) so an agent can ask "is X risky right now?" before installing. Each entry cites its GHSA primary source. Posture: TF republishes already-public advisories; the listed primary source is authoritative.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ecosystemNoOptional ecosystem filter: "npm" or "pip"
package_nameNoOptional case-insensitive substring of the package name (e.g. "mistralai" or "@mistralai/mistralai-gcp"). If omitted, returns all current entries.

TDQS

A4.2/5.0
Behavior4/5

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

Describes return content (publicly-disclosed malicious packages), matching behavior (case-insensitive substring), and source attribution (GHSA). No annotations provided, but description adequately covers behavioral traits for a read-only tool.

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?

Well-structured and informative, front-loading purpose. Could be slightly more concise, but every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, and description does not detail the fields of returned entries (only mentions GHSA source). Adequate for a simple snapshot tool, but could be more complete.

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 baseline is 3. Description adds context: ecosystem filter, case-insensitive substring, and behavior when omitted (returns all). Adds usage hint ('before installing').

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?

Clearly states it checks a feed for malicious AI-related packages, specifies the feed name (TensorFeed) and ecosystems (npm/PyPI). Distinct from sibling tools like check_agent_reputation.

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?

Explains when to use with no args (whole snapshot) vs with package_name (check specific package before installing). Lacks explicit when-not or alternative comparisons, but context is clear.

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
Disambiguation3/5

Several security/vulnerability tools (check_ai_supply_chain_risk, get_cve_record, get_osv_advisory_by_id, get_osv_advisory_for_package) have overlapping purposes, making it potentially confusing to choose the right one. Other tools are more distinct, but the ambiguity in this cluster lowers the score.

Naming Consistency3/5

All names are snake_case and mostly follow a verb_noun pattern, but the verbs are inconsistent (check, get, list, lookup, query, register, route, search, submit, whats_new). Some tools use 'get' while others use 'check' for similar retrieval actions, and 'whats_new' does not fit the verb_noun pattern.

Tool Count2/5

With 27 tools, the count exceeds the 25 threshold for 'too many', even though the broad scope spans many domains. The sheer number makes the server feel heavy and harder to navigate.

Completeness3/5

The server covers a wide range of data domains, but notable gaps exist: no management of watch subscriptions (only register), no CVE search, and no model search. Write operations are minimal, leaving some workflows incomplete.

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