CounselorAI

Letter of recommendation

rec

Draft a letter of recommendation from counselor notes. Call this to WRITE a letter. To critique one that already exists, call letterlens instead, which is much cheaper. Runs the same 5-step pipeline as appeal.

Cost: about 369 credits (~$0.369) per call.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`major`	No
`story`	No	A specific moment worth telling. The single highest-value field here.
`traits`	Yes	Three standout traits. Send prose, not a list of adjectives: the pipeline turns each one into a scene and needs something to work from.
`purpose`	No	What the letter is for. undergraduate, graduate, internship and other are legacy aliases kept working for existing integrations.	college_general
`schools`	No	Target school or program.
`profType`	No	Only when purpose is professional: which professional school, since each reads for different things.
`recipient`	No	Who receives it, if named.
`resumeText`	No	Resume or activity list as plain text.
`sampleText`	No	A sample of the counselor's own writing, so the letter lands in their voice rather than a generic one.
`selectivity`	No	How selective the target is. Shifts how much evidence the letter has to carry.
`studentName`	No	The student this is about. Used in the output text.
`contribution`	No	What the student adds to a campus.
`relationship`	No	How the counselor knows the student, and for how long.
`counselorName`	No	Who the letter is from. The counselor stays the author; this tool drafts, it does not sign.

TDQS

A4.4/5.0

Behavior4/5

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

The annotations provide only generic non-destructive flags, so the description carries the behavioral burden. It discloses cost explicitly, notes the shared 5-step pipeline with appeal, and frames the tool as drafting rather than signing. It does not describe output details, but the generative/drafting nature is adequately conveyed.

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 four short sentences with no filler: purpose, write-vs-critique routing, pipeline context, and cost. The most important distinction is front-loaded, and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 14-parameter generative tool with no output schema, the description covers the essential decision, cost, and pipeline context, while the schema handles input details. A sentence about the returned artifact being the drafted letter would increase completeness, but it is strongly implied by 'Draft a letter' and the write/critique contrast.

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 description coverage is 93%, so the baseline is 3. The description itself adds no parameter-level detail, but the schema's rich per-field notes—story as the 'single highest-value field', traits needing prose, profType only for professional purposes—cover the parameter semantics effectively.

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: 'Draft a letter of recommendation from counselor notes.' It reinforces the action with 'Call this to WRITE a letter,' clearly separating it from critique and making the tool's core purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use guidance ('Call this to WRITE a letter') and when-not-to-use guidance ('To critique one that already exists, call letterlens instead'), including the cost rationale ('much cheaper'). This is an exemplary routing statement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All CounselorAI tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.2/5.0

Disambiguation5/5

Every tool targets a distinct workflow: writing vs. critiquing letters (rec/letterlens/revision), pre-award vs. post-award aid (aid/awardlens), and text rewriting vs. translation (humanize/translate). Descriptions explicitly cross-reference related tools to prevent misselection.

Naming Consistency3/5

Names are mostly short single lowercase words, but there is no consistent verb_noun pattern: some are nouns (profile, appeal), some verbs (humanize, translate), and two use underscores (account_balance, quote_call). The conventions are readable but mixed.

Tool Count5/5

13 tools is well within the ideal range for a specialized counselor assistant. Each tool has a clear role, including two free utility tools (account_balance, quote_call) that support budgeting without bloating the core surface.

Completeness4/5

The set covers the main counselor workflows end-to-end: profile input, financial aid analysis, FAFSA checklists, appeal letters, recommendation letters, scholarships, and family-facing translation. Minor gaps exist (e.g., no dedicated college-list builder or essay drafting tool), but agents can work around them.

Resources

Need Help?