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shenchensucc

Chen's AI Copy

by shenchensucc

get_context

Retrieve professional background information including experience, skills, resume preferences, project learnings, and company interests for project planning and context understanding.

Instructions

Get Chen's full personal context: experience, skills, resume preferences, learnings, and interested companies. Use when starting a new project or when you need to understand Chen's background and preferences.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionNoOptional: 'all' | 'experience' | 'skills' | 'preferences' | 'learnings' | 'companies'. Default: all.

Implementation Reference

  • The handler logic for the 'get_context' tool, which fetches data using getFullContext and filters based on the 'section' argument.
    if (name === "get_context") {
      const section = (safeArgs.section as string) || "all";
      const context = await getFullContext();
    
      let output: unknown;
      if (section === "all") {
        output = context;
      } else if (section === "experience") {
        output = context.experience;
      } else if (section === "skills") {
        output = context.skills;
      } else if (section === "preferences") {
        output = context.preferences;
      } else if (section === "learnings") {
        output = context.learnings;
      } else if (section === "companies") {
        output = context.interestedCompanies;
      } else {
        output = context;
      }
    
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(output, null, 2),
          },
        ],
      };
    }
  • src/index.ts:31-46 (registration)
    The registration of the 'get_context' tool in the ListToolsRequestSchema handler.
    {
      name: "get_context",
      description:
        "Get Chen's full personal context: experience, skills, resume preferences, learnings, and interested companies. Use when starting a new project or when you need to understand Chen's background and preferences.",
      inputSchema: {
        type: "object",
        properties: {
          section: {
            type: "string",
            description:
              "Optional: 'all' | 'experience' | 'skills' | 'preferences' | 'learnings' | 'companies'. Default: all.",
            enum: ["all", "experience", "skills", "preferences", "learnings", "companies"],
          },
        },
      },
    },
  • The helper function 'getFullContext' that actually retrieves and aggregates the data for the 'get_context' tool.
    export async function getFullContext(): Promise<ContextData> {
      const [preferences, experience, skills, learnings, interestedCompanies] = await Promise.all([
        loadJson<Preferences>("preferences.json", {}),
        loadJson("experience.json", {}),
        loadJson("skills.json", {}),
        loadJson<Learning[]>("learnings.json", []),
        loadJson<InterestedCompany[]>("interested_companies.json", []),
      ]);
    
      return {
        preferences,
        experience,
        skills,
        learnings,
        interestedCompanies,
        updatedAt: new Date().toISOString(),
      };
    }
Behavior3/5

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

No annotations provided, so description carries full burden. Expands 'context' into concrete data categories but omits data source, freshness, return format structure, and cache behavior. Justifies a 3 by defining what constitutes 'context' beyond the tool name.

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?

Two sentences, zero waste. First sentence defines scope with enumeration; second gives usage triggers. Front-loaded with action and resource. Every clause earns its place.

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?

Simple read tool with one optional parameter. Without output schema or annotations, description adequately covers return categories but lacks return structure and data provenance. Sufficient for the complexity level but gaps remain.

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

Parameters3/5

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

Schema coverage is 100% with clear enum documentation. Description lists the section values ('experience', 'skills', etc.) in prose but doesn't explicitly reference the 'section' parameter or explain filtering syntax. Baseline 3 appropriate given schema does heavy lifting.

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?

Uses specific verb 'Get' with explicit resource 'Chen's full personal context'. Enumerates specific components (experience, skills, resume preferences, learnings, interested companies) that distinguish this read operation from write-oriented siblings (add_interested_company, add_learning, update_preference).

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?

Provides explicit when-to-use guidance ('when starting a new project' and 'when you need to understand Chen's background'). Lacks explicit when-not-to-use or named alternatives, but clear positive guidance earns a 4.

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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