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VarynForge

Get pitch report payload

get_pitch_report_payload
Read-only

Assemble the client-ready pitch report payload for a completed research run — niche + solution profile, competitor landscape, ranked opportunity map holding GAPS ONLY (0-100 normalized; demand sized as volumeBucket buckets, no search-volume numbers by design; each entry carries overlapStatus net_new|partial plus siteOverlap — covered-keyword share + closest owned pages), coveredOpportunities as a separate defend-and-refresh list (topics the site already covers — never pitch these as new work), and a derived 90-day content sequence sized to the project posting cadence (postsPerWeek, up to 12 weeks; planned articles first, then gap topics marked type: topic — topics to develop, not article titles; covered clusters never enter the sequence; entries may carry advisory notes — near-duplicates, already-published near-matches, working titles needing a rewrite — keep the notes visible when rendering). assetProfile reports when the profile facts were last synced; when assetProfile.syncedBeforeRun is true, offer the operator the choice between resync_asset_profile first and rendering with the profile as-is, and always render the "Asset profile last synced " stamp. renderGuide carries the full report structure with an explicit branding slot: render it in the operator’s own template (Docs, Slides, Notion, or PDF) under the operator’s name. Included with every run, no extra cost. Pass the runId from get_research_status; a run that is not completed returns { runStatus, report: null }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
runIdYes

TDQS

B3.4/5.0
Behavior3/5

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

The description discloses that the payload includes assetProfile, renderGuide, and conditions like 'syncedBeforeRun' and 'offer the operator the choice', which adds behavioral context. However, the excessive repetition and inclusion of rendering instructions (which are more about downstream usage than the tool's own behavior) dilute the transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is an extremely long, run-on sentence with repeated phrases like 'no search-volume numbers by design' and 'under the operator's name'. It is poorly structured and overly verbose, making it hard to parse. The key information could be conveyed in a few concise sentences.

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?

The description covers numerous aspects: report structure, assetProfile, renderGuide, and conditions, making it fairly complete. However, the information is presented in a disorganized manner, and some elements (like rendering instructions) may be irrelevant to the API call itself, so completeness is achieved at the cost of clarity.

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?

The only parameter, runId, is given meaningful context in the description ('Pass the runId from get_research_status'), which helps the agent source it correctly. This adds value beyond the bare schema requirement of a non-empty string.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action: 'Assemble the pitch report payload' for a completed research run, which distinguishes it from other get_* tools. However, the verbosity obscures the primary purpose, making it less crisp than ideal.

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?

It explicitly says 'for a completed research run' and instructs to pass the runId from get_research_status, including handling for incomplete runs (returns null). This provides clear guidance on when to use it, though it stops short of naming alternative tools for other scenarios.

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

A3.8/5.0
Disambiguation4/5

Most tools have distinct purposes, but a few pairs could confuse an agent: add_article_suggestion vs create_article_suggestion_with_input, and get_article_brief vs download_brief_markdown vs get_write_handoff all deal with brief content. The detailed descriptions help disambiguate, but the overlap is real.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in lowercase snake_case (create_project, list_opportunities, generate_article_brief, lint_draft). There is no mixing of camelCase, acronyms, or vague verbs, making the naming predictable and readable.

Tool Count2/5

50 tools is excessive for an MCP server, even for a broad platform like content operations. While the scope is large, this many tools will overwhelm agents and increase latency and context cost. Most practical servers are well under 25.

Completeness4/5

The tool surface covers the full content lifecycle: project creation, research, opportunity clustering, content planning, briefs, drafting, linting, publishing, and reporting. Minor gaps exist (e.g., no delete_project, no remove_destination, no direct analytics beyond distributions), but they are workarounds or handled in the web UI.

Resources