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create_product

Create a new product, run analysis, and return its initial stats.

    ``config_upload_id`` references a previously-staged .config that the
    caller POSTed to ``/api/configs/uploads`` over plain HTTP — the LLM
    does NOT emit the config text itself (a real kernel .config is
    ~100–200 KB and exceeds a single tool-call output budget). Workflow:

    1. Caller / wrapper script:
       ``curl -H "Authorization: Bearer ks_live_..." \
              -F "config_file=@.config" \
              https://kernelscan.io/api/configs/uploads``
       returns ``{config_upload_id, sha256, size_bytes, expires_at}``.
    2. Pass that ``config_upload_id`` into this tool.

    Uploads are per-user, single-use, and expire 30 minutes after upload.
    Same gates as POST /api/products: free can't create products; paid
    plans are capped at their resolved product limit — read it (and any
    per-account override) from ``whoami.product_limit`` rather than assuming
    a fixed per-tier number. ``factor_ids`` are silently ignored unless the
    plan allows security factors (``whoami.can_use_factors``). Re-using a
    product name returns 409.

    Creating a product RUNS an analysis, so it spends one unit of the
    team's SHARED monthly analysis allowance (``whoami.monthly_analyses_used``
    / ``monthly_analyses_limit``). When the allowance is exhausted the tool
    fails with "Monthly analysis limit reached (…/month) [429]". This is a
    durable monthly quota — NOT the transient per-call rate limit that also
    surfaces as 429: it will not clear until next month, so report it to the
    user instead of retrying. Check ``whoami`` before a batch of creates.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
archYes
nameYes
factor_idsNo
descriptionNo
kernel_versionYes
config_upload_idYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and excels. It discloses that creation triggers an analysis consuming shared quota, that uploads are single-use and expire, that factor_ids are silently ignored without plan permission, and distinguishes durable monthly quota from transient rate limits. This is comprehensive.

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 structured with a summary, numbered workflow steps, a code example, and warnings. It is long but every sentence earns its place given the tool's complexity. Front-loaded with the core purpose and then details.

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?

For a complex tool with prerequisites, plan gates, quotas, and error modes, the description covers all necessary context. It also notes the return value ('initial stats'), which is helpful given no output schema. Overall, it is complete enough for an agent to use correctly.

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 0%, so the description must compensate. It provides deep detail on config_upload_id (how to obtain, expiry, workflow) and factor_ids (plan-gated behavior). Other parameters like name, kernel_version, arch are self-explanatory, but the critical one is fully covered.

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 'Create a new product, run analysis, and return its initial stats.' This is a specific verb+resource+output combination. It distinguishes itself from sibling tools like update_product, get_product, and list_products by focusing on creation and analysis.

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?

The description provides an explicit workflow: upload a config first via curl, then pass the resulting config_upload_id. It explains plan restrictions, quota limits, and error handling (409 for duplicate names, 429 for monthly limit). It even advises checking whoami before batch creates, which is actionable 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

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: product lifecycle (create/get/list/update), CVE lookup/search, VEX retrieval, access request, support submission, and identity/quota check. There is no overlap; even create_product and update_product are clearly differentiated by their operation semantics.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (create_product, get_cve, list_products, etc.) using lowercase with underscores. The only outlier is whoami, which is a standard Unix-style command and fits naturally.

Tool Count5/5

10 tools is well-scoped for the domain of Linux kernel security scanning and CVE/product management. Each tool serves a clear purpose without redundancy or bloat, covering the core workflows without overwhelming the agent.

Completeness4/5

The tool set covers the major lifecycle for products (create, get, list, update) and CVEs (search, get), plus VEX retrieval and account context. Minor gaps exist such as product deletion or direct config upload, but these can be worked around via existing tools and the REST API.

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