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Rafe Blandford — career and writing

by com.rafeblandford

Get published evidence

get_evidence
Read-onlyIdempotent

Return one section of Rafe Blandford's published record.

Sections:

  • profile — who he is, what he is looking for, declared expertise and skills, awards, education, identifiers and how to contact him.

  • career — roles, dates, scope and highlights. Exactly what the /career/ page shows.

  • case-studies — the ten published case studies with their summary, lead outcome and a markdown_url for the full post; plus engagements named without a case study, which carry NO outcome claims. ⚠️ Case studies carry TWO kinds of claim. summary and lead_outcome come from the published post, so a reader can go and check them. outcomes is Rafe's own fuller account — self-attested, and it may go further than the post does. Each entry says so in outcomes_evidence_basis. Attribute it that way rather than as published fact, and never blend the two into one figure.

  • provenance — how authorship is labelled on this site, and the usage terms for the content.

For writing, use search_writing to find a slug and get_post to fetch it. Everything returned is already public on rafeblandford.com.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionYesWhich section to return. Each is identical to the resource of the same name.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds critical behavioral nuance: it warns that case-study outcomes are self-attested and may exceed published claims, clarifies the evidence basis field, and explicitly states that all returned content is publicly available. This goes far beyond what annotations provide.

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 long but every sentence serves a purpose. It is front-loaded with the core action, uses bulleted sections for clarity, and includes a critical warning that is essential for correct usage. No filler or redundant content.

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?

Given the tool's complexity (multiple sections, nuanced evidence claims) and the presence of an output schema and annotations, the description fully equips the agent. It covers all sections, explains data provenance, and directs to other tools when appropriate. No important information is missing.

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

Parameters5/5

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

The single parameter 'section' is enum-constrained and schema-described, but the description dramatically enriches each enum value with detailed content breakdowns (e.g., profile includes identifiers and contact info; case-studies warns about claim types). This gives the agent meaning well beyond the schema's one-line description.

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 opens with a specific verb and resource: 'Return one section of Rafe Blandford's published record.' It clearly distinguishes from sibling tools by explicitly directing writing-related tasks to search_writing and get_post, and the section enumeration makes its scope unambiguous.

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 and explicitly names alternatives for writing-related tasks ('use search_writing to find a slug and get_post to fetch it'). It does not mention check_experience or state explicit exclusions, but the guidance is clear enough for an agent to decide when this tool is appropriate.

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

A4.7/5.0
Disambiguation5/5

Each tool serves a distinct purpose: check_experience answers whether a topic is evidenced, get_evidence returns predefined sections, search_writing performs keyword search, and get_post retrieves full posts by slug. There is no meaningful overlap that would cause confusion.

Naming Consistency5/5

All tool names follow the verb_noun pattern (check, get, get, search). The style is consistent and intuitive, with no mixing of conventions or vague verbs.

Tool Count5/5

Four tools is well-scoped for a personal career and writing site. Each tool is necessary and has a clear role, covering both structured queries (evidence, sections) and content retrieval (search, fetch).

Completeness4/5

The tool surface covers the core workflows: checking experience, retrieving profile/career/case studies, searching writing, and fetching full posts. A minor gap is the lack of a way to enumerate all posts or browse the full archive beyond the most recent items returned by a blank search.

Resources