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product_load_context

Read-onlyIdempotent

Load Lenny Zeltser's product strategy context for local analysis. Returns expert strategic frameworks, principles, and guidance for evaluating or creating security product plans. Includes rating-sheet items (the lens taxonomy: structure, words, tone) as concrete reference points for grounded feedback on the plan's writing. This server never requests your plans and instructs your AI to keep them local. Use detail_level to control response size: "minimal" (~2k tokens), "standard" (~5k tokens), "compact" (~3-4k tokens, all sections but stripped), or "comprehensive" (~12k tokens). Use market_segment: "smb" for SMB-specific guidance. Use product_focus: "endpoint" for endpoint security viability assessment. Set include_template: true to include the fill-in-the-blank template in the response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicsNoSpecific topics to include. Overrides detail_level for fine-grained control.
detail_levelNoLevel of detail to return. "minimal": market + capabilities only (~2k tokens). "standard": core strategy sections (~5k tokens, default). "compact": all sections with stripped subsections (~3-4k tokens, good for batch analysis). "comprehensive": everything + examples (~12k tokens).
analysis_modeNo'internal': planning your own product (default). 'external': evaluating another company from outside. External mode reframes questions and adjusts evidence standards.
product_focusNoInclude vertical-specific guidance. 'endpoint': platform entrapment, defensibility. 'ai_security': AI threat landscape, buyer personas, regulatory alignment.
market_segmentNoInclude SMB-specific guidance (distribution, buying triggers, readiness).
company_contextNoFilter guidance to startup or large company perspective. Stage values (pre_seed, seed, series_a, series_b, growth, late_stage) imply startup context with stage-specific emphasis.
include_examplesNoInclude examples in framework sections. Default: false.
include_templateNoInclude the fill-in-the-blank strategy template at the end of the context response. Default: false. Saves a separate product_get_template call.
evaluation_perspectiveNoEmphasize framework sections relevant to a specific perspective. Composes with analysis_mode.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / topics / items / enum
      Previous value: -[
-  "market",
-  "capabilities",
-  "sales",
-  "pricing",
-  "delivery",
-  "trust",
-  "platform",
-  "team",
-  "competitive",
-  "integrations",
-  "all"
-]New value: +[
+  "market",
+  "capabilities",
+  "sales",
+  "pricing",
+  "delivery",
+  "trust",
+  "platform",
+  "team",
+  "competitive",
+  "defensibility",
+  "integrations",
+  "all"
+]
  2. Changed8 schema fields changed
    • addedInput schema / properties / analysis_mode
      Added value: +{
+  "description": "'internal': planning your own product (default). 'external': evaluating another company from outside. External mode reframes questions and adjusts evidence standards.",
+  "enum": [
+    "internal",
+    "external"
+  ],
+  "type": "string"
+}
    • changedInput schema / properties / company_context / description
      Previous value: -"Filter guidance to startup or large company perspective."New value: +"Filter guidance to startup or large company perspective. Stage values (pre_seed, seed, series_a, series_b, growth, late_stage) imply startup context with stage-specific emphasis."
    • changedInput schema / properties / company_context / enum
      Previous value: -[
-  "startup",
-  "large_company"
-]New value: +[
+  "startup",
+  "large_company",
+  "pre_seed",
+  "seed",
+  "series_a",
+  "series_b",
+  "growth",
+  "late_stage"
+]
    • changedInput schema / properties / detail_level / description
      Previous value: -"Level of detail to return. \"minimal\": market + capabilities only (~2k tokens). \"standard\": core strategy sections (~5k tokens, default). \"comprehensive\": everything + examples (~12k tokens)."New value: +"Level of detail to return. \"minimal\": market + capabilities only (~2k tokens). \"standard\": core strategy sections (~5k tokens, default). \"compact\": all sections with stripped subsections (~3-4k tokens, good for batch analysis). \"comprehensive\": everything + examples (~12k tokens)."
    • changedInput schema / properties / detail_level / enum
      Previous value: -[
-  "minimal",
-  "standard",
-  "comprehensive"
-]New value: +[
+  "minimal",
+  "standard",
+  "compact",
+  "comprehensive"
+]
    • addedInput schema / properties / evaluation_perspective
      Added value: +{
+  "description": "Emphasize framework sections relevant to a specific perspective. Composes with analysis_mode.",
+  "enum": [
+    "builder",
+    "analyst",
+    "investor",
+    "buyer"
+  ],
+  "type": "string"
+}
    • changedInput schema / properties / product_focus / description
      Previous value: -"Include endpoint security viability assessment (platform entrapment, defensibility)."New value: +"Include vertical-specific guidance. 'endpoint': platform entrapment, defensibility. 'ai_security': AI threat landscape, buyer personas, regulatory alignment."
    • changedInput schema / properties / product_focus / enum
      Previous value: -[
-  "endpoint"
-]New value: +[
+  "endpoint",
+  "ai_security"
+]
  3. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond annotations: it never requests plans and instructs AI to keep them local, a privacy-related trait. It also describes output size behavior via detail_level token counts, which is not in annotations.

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 fairly long but efficiently front-loaded with purpose and then parameter details. Every sentence adds information about usage, privacy, or parameter behavior. It is appropriately sized given 9 parameters, though a slightly more concise version might exist.

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?

Despite lacking an output schema, the description mentions what is returned (frameworks, rating-sheet items, lens taxonomy) and covers privacy, parameter selection, and relationships to other tools (e.g., include_template saves a product_get_template call). No important aspect seems missing for a context-loading tool.

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. The description adds extra meaning: detail_level token estimates ('~2k tokens', '~5k tokens') and compact meaning 'all sections but stripped', market_segment 'smb' for SMB-specific guidance, product_focus 'endpoint' for endpoint security viability. This exceeds what the schema alone provides, justifying a 4.

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 explicitly states 'Load Lenny Zeltser's product strategy context for local analysis' and describes what it returns: 'expert strategic frameworks, principles, and guidance'. This is a specific verb + resource + purpose, clearly distinguishing it from siblings like product_compare_context and product_review_plan.

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?

The description provides clear context for when to use this tool, such as 'This server never requests your plans and instructs your AI to keep them local' and parameter guidance for controlling response size. It also mentions 'Saves a separate product_get_template call' for include_template, naming an alternative. However, it doesn't explicitly exclude other contexts or 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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