Skip to main content
Glama
seinun-ai

Maestro Career Studio

Official
by seinun-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Edit Tailored Resume

edit_application
Destructive

Apply typed edits to an application's current tailored resume, preserving prior changes and avoiding duplicate applications; falls back to base resume only when none exists.

Instructions

INCREMENTAL FIX: apply typed edit ops on top of an existing application's CURRENT tailored resume (customized_json), preserving every prior edit.

Falls back to the base resume only when the application has no customized_json
yet (nothing to preserve). Suited to refining or retrying a tailored resume
after inspecting PDF previews — swap a bullet, tweak wording, fix one thing —
without losing the rest of what's already been tailored and without creating
duplicate applications. tailor_application is the from-scratch alternative:
it recomputes customized_json off the BASE resume and discards any tailored
edits already on the application.
Accepts the FULL op vocabulary documented on edit_base_resume — every
kind. replace_entry changes an entry's dates, title, company, project name
or tech list; add/replace/remove/move_extra_section handle the custom
Publications/Awards/Volunteer sections.

Op field names (these exact keys, no synonyms):
{"kind": "replace_bullet", "section": "experience", "index": 0,
 "bullet_index": 2, "value": "New bullet text"} — `kind` (not op/type),
`index` (not entry_index; 0-based into the FULL array incl. disabled rows),
`value` (not text).
Index is 0-based into the full customized_json array (including enabled:false),
as shown by get_application, not PDF ordinals. Response `applied[].name`
echoes the entry each op touched.
Clears pdf_path/tex_path (the old PDF is stale); render_pdf produces a new one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
opsYesTyped edit ops. Each is an object with a `kind`, one of: replace_summary | toggle_entry | replace_bullet | replace_skills_group | add_skill_item | add_bullet | add_entry | replace_entry | remove_entry | remove_bullet | replace_contact | replace_certifications | add_extra_section | replace_extra_section | remove_extra_section | move_extra_section. To change a field ON an entry — dates, title, company, project name, link, tech — use replace_entry with that entry's index and the full edited entry object; it is scoped to that one entry (a whole-resume replace is the separate update_base_resume tool). See the tool description for each op's shape. add_extra_section/replace_extra_section `value` is {key,title,enabled,type:"entries"|"bullets"} plus EITHER entries:[{heading,subheading?,location?,date?,link?,enabled,bullets:[str]}] OR bullets:[str] — never both; unknown keys are rejected (400, extra="forbid").
application_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.11.1

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true, idempotentHint=false, readOnlyHint=false, but the description adds real operational context beyond them: prior edits are preserved incrementally, the base-resume fallback only fires when customized_json is absent, and pdf_path/tex_path are cleared because the old PDF is stale (with render_pdf as the recovery). It doesn't cover auth/permission requirements or how a partially-failing op batch is reported, keeping it short of a 5.

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?

Front-loaded with the mode ('INCREMENTAL FIX') and then layers fallback, usage, alternative, op vocabulary, and field-name constraints in a logical order. Every sentence carries operational weight, though the op-vocabulary and key-name sections are dense enough that a couple of clauses could be tightened.

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?

There is no output schema, but the description compensates by documenting the response shape (`applied[].name` echoes each touched entry) and the side effect on pdf_path/tex_path plus the render_pdf recovery. Nothing critical for correct invocation is missing; only error/partial-failure semantics go unaddressed.

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 only 50% (application_id has no schema description), so the description must compensate, and it does: it pins exact key names ('kind' not op/type, 'index' not entry_index, 'value' not text), specifies 0-based indexing into the FULL array including enabled:false rows as shown by get_application, and points to edit_base_resume for the full op vocabulary. This maps directly onto the loosely-typed ops array and adds meaning the schema alone does not convey.

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?

States a specific verb+resource and a precise operation mode: 'apply typed edit ops on top of an existing application's CURRENT tailored resume (customized_json), preserving every prior edit.' It explicitly distinguishes itself from tailor_application (from-scratch) and update_base_resume (whole-resume replace), so an agent can disambiguate without opening schemas.

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?

Gives explicit when-to-use ('refining or retrying a tailored resume after inspecting PDF previews — swap a bullet, tweak wording, fix one thing') and names the alternative with the condition that selects it: tailor_application recomputes off the BASE resume and discards tailored edits. Also covers the no-customized_json fallback case. Routing is fully specified.

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

Deploy Server

Other Tools