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Nullcone Threat Intelligence

by io.github.maco144

fingerprint_tool_metadata

Analyze an MCP tool definition for instruction-injection and malicious patterns.

Performs semantic fingerprinting of the tool's description, parameter schemas,
and error templates — detecting credential exfiltration vectors, C2 callbacks,
base64 payloads, authority spoofing, and injection phrase patterns.

Also checks the tool hash against the SKILL IOC feed and the description
against the PROMPT IOC feed for known-malicious matches.

If track=True (default), the tool definition is compared against a stored
baseline and semantic drift is detected on subsequent calls for the same tool.

Args:
    tool_def: MCP tool definition dict. Expected keys: name, description,
              inputSchema (optional), annotations (optional).
    registry: Registry this tool came from ("mcp.so", "clawhub", "smithery",
              "npm", "pypi", "github", or "unknown").
    track:    If True, maintain baseline and detect drift across calls.

Returns:
    tool_name:        Tool name
    tool_hash:        SHA256 of canonical tool definition
    risk:             "clean" | "low" | "suspicious" | "malicious"
    risk_score:       0.0–1.0
    should_block:     True if risk == malicious
    should_warn:      True if risk >= suspicious
    signals:          List of detected signals with field, pattern, excerpt
    prompt_ioc_matched: True if description matched PROMPT IOC feed
    skill_ioc_matched:  True if tool hash matched SKILL IOC feed
    latency_ms:       Analysis latency
    drift:            Drift result (if track=True and tool was seen before)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
trackNo
registryNounknown
tool_defYes

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full transparency burden. It does well by describing the analysis behavior, IOC feed checks, default track=True behavior, stored baseline/drift logic, and the returned verdict fields. It does not fully explain whether tracking itself writes persistent state or whether IOC feed checks involve external calls, but these are relatively minor gaps.

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 well-organized: overview, behavioral details, Args, and Returns. Every section contributes necessary context, and there is no fluff. It is slightly dense because it enumerates many return fields, but that is justified by the lack of an output schema.

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?

Given the tool's complexity, the absence of annotations, and the lack of an output schema, the description is highly complete. It covers the tool's purpose, detection categories, parameter semantics, optional tracking behavior, and all important return fields including risk, score, block/warn flags, signals, IOC matches, latency, and drift.

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

Parameters5/5

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

The input schema provides names and defaults but no descriptions, and schema coverage is 0%. The description fully compensates with an Args section that explains each parameter: tool_def with expected keys, registry with valid source values, and track with its behavioral meaning. This gives an agent the semantics needed to build a correct call.

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 opens with a clear, specific action: 'Analyze an MCP tool definition for instruction-injection and malicious patterns.' It then lists concrete detection targets such as credential exfiltration, C2 callbacks, base64 payloads, authority spoofing, and injection phrases, which makes the tool's purpose unmistakable and distinguishes it from sibling tools that scan content or manage threat feeds.

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

Usage Guidelines3/5

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

Usage context is implied: the user should call this when they have an MCP tool definition and need a malicious-pattern/risk analysis. However, the description does not explicitly state when to use this tool vs alternatives such as check_prompt, scan_skill_content, or validate_skill, nor does it mention exclusions or preferred 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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is notable overlap among threat-fetching methods: get_new_threats, poll_since, and drain_subscription all retrieve new threats via different mechanisms, and several stats tools (get_stats, freshness_limits, prompt_cache_stats) serve similar informational roles. Overall, descriptions help disambiguate, but a few tools could be confused.

Naming Consistency3/5

The naming is predominantly snake_case, but mixes verb_noun (check_freshness, submit_ioc), noun phrases (family_threats, freshness_limits), and even a question (is_ioc_revoked). 'unsubscribe' breaks the pattern of other subscription tools (subscribe_threats, drain_subscription). This inconsistency is noticeable though still readable.

Tool Count3/5

30 tools is on the heavy side for a single MCP server, exceeding the typical 15-tool comfort zone. However, the server covers a broad domain—IOC submission, retrieval, subscriptions, freshness tracking, prompt/skill scanning, and registry monitoring—so the large number is somewhat justified by the scope.

Completeness4/5

The tool surface covers the full threat intelligence lifecycle: submit (submit_ioc, submit_batch), query (lookup_ioc, search_by_type, recent_threats, family_threats), subscribe (subscribe_threats, drain_subscription), update (report_detection, vote_false_positive), and revoke (revoke_ioc). Minor gaps exist, such as the absence of a direct delete or update signature tool and no get-by-signature-id endpoint, but these are manageable for agents.

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