Get the owner's professional profile summary, plus nextSteps — concrete ways to continue (contact channels, GitHub, MCP connect). Use it as the first tool in an interview.
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes the safe, non-mutating nature of the tool. The description adds useful context about what the result contains (profile summary and nextSteps with contact channels, GitHub, MCP connect). No behavioral contradictions or hidden side effects are present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences carry the full purpose plus a usage directive. The most important information is front-loaded, and every phrase adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no parameters, no output schema, and read-only annotations, the description fully covers what the tool returns and when to invoke it. An agent has everything needed to correctly select and call this tool in the intended interview flow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema fully defines the invocation surface. The description doesn't need to explain parameters, and the baseline of 4 applies because there is nothing missing for the agent to call it correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific verb ('Get'), a distinct resource ('the owner's professional profile summary'), and adds the supplementary 'nextSteps' content. It is clearly differentiated from sibling tools like get_projects or get_timeline, which target different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use the tool: 'Use it as the first tool in an interview.' This is strong contextual guidance. It does not mention alternatives or when not to use it, but given the unique profile resource, exclusions are less necessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map cleanly to distinct content types such as profile, projects, issues, diary, experiments, and live-source verification. A few pairs like get_projects/search_projects and get_articles/verify_article touch the same subject matter, but their descriptions clarify the intended action well enough for an agent.
All tools follow a consistent snake_case verb_noun pattern: get_* for portfolio content, verify_* for external grounding, plus analyze_stack, search_projects, and simulate_architecture. There is no mixing of conventions or vague generic verbs.
18 tools is on the higher end but justified by the portfolio's breadth: content domains, project search/simulation, and open-world verification all have distinct needs. It is slightly heavy but not bloated; each tool has a discernible reason to exist.
The surface covers the full portfolio/interview domain: profile, projects, timeline, articles, repos, packages, issues, engineering history, experiments, principles, and verification. It also includes grounding against live sources, leaving no obvious dead ends for an agent answering questions about the owner.