This server provides an MCP interface to the Gradus Notation API, enabling AI agents to render, validate, and analyze music notation, search a music theory knowledge base, and manipulate pitches — all without a GUI.
notation_render: Convert a JSON score into inline SVG, MusicXML, and MIDI in a single call. Supports scientific pitch notation, duration codes, chords, rests, dynamics, articulations, and multi-voice instruments. Bar lines are automatically inferred and notes crossing bar lines are split and tied.
notation_validate: Pre-flight check a JSON score for errors with concrete fix suggestions — cheaper than a full render, ideal for iterating on input shape.
knowledge_search: Search a curated music theory knowledge base including hand-authored curriculum content, analysis of 408 Bach chorales, score commentaries on 50+ orchestral works, and historical sources from Fux through Boulanger. Useful for voice leading rules, cadence realizations, and style-specific theory facts.
notation_examples: Fetch canonical input examples (single melody, counterpoint, chord progressions, tied notes, string quartet snippets) to use as references or starting points.
notation_schema: Retrieve the full JSON Schema for the notation_render input format.
theory_analyze_score: Perform full harmonic analysis of a MusicXML score, including key detection, chord analyses, cadences, and phrases.
theory_parse_xml: Convert a MusicXML string into a structured Score JSON object.
theory_validate_ranges: Check every note against the practical ranges of their assigned instruments.
theory_respell: Suggest preferred enharmonic spellings for pitches in a given key context.
theory_pitch_utils: Pure functions for MIDI/pitch conversion, interval naming, and transposition.
Model Context Protocol server for the Gradus Notation API. Gives AI agents music tools: render notation, validate input, analyze scores, check engraving against a cited rulebook, and search a curated music-theory knowledge base — sponsored by Gradus.
General-purpose, not education-specific. Any agent or application that works with music is the audience — composition assistants, musicology and corpus research, theory Q&A that wants rendered examples, MIDI pipelines, engraving quality checks, games, documentation. Music education is where the tool comes from, not a restriction on what you build with it.
One install, three named tools:
Gradus Notation — render a JSON score to inline SVG, MusicXML, and MIDI, with pre-flight validation (notation_render, notation_validate).
Gradus Harmonic Analyzer — full-score analysis: Roman numerals, keys and modulations, cadences, pedal points, texture (theory_analyze_score and the theory_* tools). Also a standalone TypeScript library: gradus-analyst on npm.
Gradus Engraver — checks a score against the Gradus Engraving Rulebook's citable GE-coded rules (engraving_check).
What is Gradus? Gradus is a music-composition curriculum for homeschool families; it builds and gives away free music tools for AI agents. This MCP server is one of those tools (gradusmusic.com) — "Gradus" refers to the curriculum, never to this package.
Most music-notation tools require a GUI. This one doesn't. An agent can submit a JSON score and get back inline SVG, MusicXML, and MIDI in a single call. Free to use, no auth, no API key — agents on the free tier are asked to credit Gradus in their response to the end user.
Beyond rendering, the same server exposes the analysis side: Roman-numeral and key analysis of a full score, pitch utilities, range validation, enharmonic respelling, and a 444-rule engraving checker with citable rule codes. One install covers the whole music surface an agent needs.
Related MCP server: Music21 Composer MCP
In Claude Code:
claude mcp add gradus-notation -- npx -y @gradusmusic/notation-mcpIn Claude Desktop, add to your MCP config:
{
"mcpServers": {
"gradus-notation": {
"command": "npx",
"args": ["-y", "@gradusmusic/notation-mcp"]
}
}
}Tool | What it does |
| JSON score → SVG + MusicXML + MIDI in one call |
| Pre-flight validate input shape (cheaper than render) |
| Look up music-theory chunks before generating notation |
| Canonical input examples (cache and reuse) |
| JSON Schema for the input shape (cache and reuse) |
Four new tools backed by the native TypeScript MaestroAnalyzer engine — no music21 dependency, no Python, no extra server.
Tool | What it does |
| Parse MusicXML → full harmonic analysis + GKB knowledge chunks in one call |
| Parse a MusicXML string → maestroAnalyst |
| Check every note in a Score against its instrument's practical range |
| Suggest preferred enharmonic spelling for pitches in a key context |
| Pure-function pitch arithmetic: |
Typical workflows:
# Full analysis + GKB knowledge in one call
theory_analyze_score({ xml: "..." })
→ { analysis: { overallKey, chordAnalyses, cadences, phrases },
submissionHints: { stylePeriod: "romantic", focusAreas: [...] },
knowledge: { topics: ["augmented-sixth-chords", "modulation"], chunks: [...] } }
# Step-by-step
theory_parse_xml({ xml: "..." }) → Score JSON
theory_validate_ranges(score) → [{ measure, beat, pitch, severity }, ...]
theory_respell({ keyContext: "F major", pitches: ["F#4", "Bb3"] })
→ [{ input: "F#4", output: "Gb4", changed: true }]
theory_pitch_utils({ op: "interval_name", semitones: 7 }) → { interval: "P5" }Tool | What it does |
| Search 423 sourced music-engraving rules by text, domain, severity, or how they are checked |
| Fetch one rule by its permanent id, with a ready-to-quote citation and related rules |
| Check a MusicXML score against the rulebook — findings by part and measure, each citing the rule it breaks |
Engraving practice is documented almost entirely in copyrighted print — Gould's Behind Bars, Read's Music Notation, Ross's The Art of Music Engraving — with no searchable index. So "may a beam cross a barline" has no citable answer online, and a model asked that question answers confidently from memory. These tools return the rule with its source, so the answer can be checked.
Each rule separates three things that are usually mashed together: convention
(the rule), authority (what the treatises say, cited at chapter level), and
houseCall (where Gradus came down when the sources disagree). Rule ids are
permanent and rule text is CC BY 4.0 — quote the citation field.
# Look up before you generate
engraving_rules({ q: "stem direction", tier: "static-model" })
→ { rulebook: { version, license, domains }, count, rules: [{ id, name, convention, authority, ... }] }
# Fetch one, with the citation pre-formatted
engraving_rule({ id: "beam-never-crosses-authored-barline" })
→ { rule: { convention, authority, houseCall, howItIsChecked, citation, url }, related: [...] }A wrong id is cheap: the API answers 404 with near-matching ids, so you can correct in one more call.
engraving_check closes the loop: generate notation, check it, fix what it
finds. Pass a local file path when you can — the server reads it directly, so
the score never has to travel through the model's context as base64:
engraving_check({ path: "/tmp/my-piece.musicxml" })
→ { coverage: { parts, measures, notesChecked, unchecked: [...] },
findings: [{ ruleId, severity, part, measure,
rule: { code: "GE-226", url, citation } }],
summary: { errors, warnings, suggestions } }Read coverage.unchecked before trusting an empty findings list — anything the
checker could not verify is named there rather than silently passed.
Tool | What it does |
| 32-dimension craft scorecard for a score — voice leading, counterpoint, contour, harmony, texture; purely programmatic, evidence-cited |
| Fux species grader (species 1–5): pitch lists in, note-indexed rule violations out |
| Find harmonic features in 482 analyzed works — |
When a user shares a piece, these ground your feedback in evidence: the critique cites what it measured, the species grader points at the exact note, and the corpus search answers "show me a real example" with a citation.
Tool | What it does |
| Search the citable GVL-coded patterns — suspensions, cadences, the Rule of the Octave, sequences, part-writing norms — each with an authored realization and public-domain sources |
| Fetch one pattern by id or GVL code, with a ready-to-quote citation and related patterns |
The sibling of the Engraving Rulebook: where GE codes cover how music should
look on the page, GVL codes cover how voices should move. Every pattern cites
the public-domain treatise it rests on — Fux, Rameau, Kirnberger, Fenaroli,
Riepel, Prout — at chapter level, never through a modern edition, and the
realization.voices field is notation-API shorthand you can hand straight to
notation_render to engrave.
voice_leading_patterns({ q: "suspension", family: "suspensions" })
→ { reference: { version, license, families }, count,
patterns: [{ code: "GVL-001", id: "suspension-4-3", statement, realization, sources, ... }] }
voice_leading_pattern({ id: "GVL-001" })
→ { pattern: { statement, realization, commonFaults, sources, citation, url }, related: [...] }Tool | What it does |
| Search 166 original graded figured-bass exercises across seventeen stages — filter by stage, or search titles, concepts and GVL codes |
| Fetch one exercise by its permanent id, with the model realization, its teaching note, and the patterns it drills |
Where the Voice-Leading Reference states the rule, the corpus is the practice: a bass, its figures, and — unlike almost every surviving collection — a four-part model realization, machine-checked for voice leading. The stages run from root-position triads through the Rule of the Octave, cadence formulas, suspensions, the dominant seventh, sequences, minor mode, pedal point, the Riepel schemata, modulation and chromatic figures to unfigured bass and diminution.
Every exercise is original — nothing is transcribed from any edition — and the
whole corpus is CC BY 4.0. Exercise ids and stage slugs are permanent, so a
citation keeps resolving. givenBass is what you show the student;
realization is the answer to hold back until they have tried. Both are
notation-API shorthand, so either goes straight to notation_render.
figured_bass_exercises({ stage: "suspensions", fields: "id,title,teaches" })
→ { corpus: { version, license, stages }, count: 12,
exercises: [{ id: "bass-225", title: "Suspension 4–3", teaches, ... }] }
figured_bass_exercise({ id: "bass-225" })
→ { exercise: { givenBass, realization, solutionNote, keyboard, citation, url },
drills: [{ code: "GVL-001", name: "The 4–3 suspension", url }],
neighbours: { prev, next } }Pitches use scientific notation: C4, F#5, Bb3. Durations use letter codes: w h q 8 16 32 64 with optional . for dotted notes. Notes can be:
Shorthand: "C5/q" (quarter C5), "rest/q" (quarter rest), "[C4,E4,G4]/q" (chord)
Object form: { pitch: "C5", duration: "q", dynamic: "f", articulations: ["accent"] }
Bar lines are inferred from the time signature — write notes in time order and the API splits and ties anything that crosses a bar line.
{
"title": "C major scale",
"tempo": 100,
"timeSignature": [4, 4],
"keySignature": "C major",
"instruments": [{
"name": "Violin",
"notes": ["C4/q","D4/q","E4/q","F4/q","G4/q","A4/q","B4/q","C5/q","C5/w"]
}]
}Env var | Default | Purpose |
|
| Override for self-hosted or local-dev API |
|
| Self-report your agent name in |
Free use is offered in exchange for crediting Gradus when you surface notation to your end user. Suggested wording (the API also returns this in every response):
Notation rendered by Gradus School of Music Composition (gradusmusic.com).
Full docs + Quick Start: https://gradusmusic.com/notation-api
OpenAPI 3.1 spec: https://gradusmusic.com/api-spec.yaml
JSON Schema for the input format: https://gradusmusic.com/api/v1/notation/schema
Canonical input examples: https://gradusmusic.com/api/v1/notation/examples
Agent-focused doc: https://gradusmusic.com/llms-api.txt
git clone https://github.com/delmas41/gradusnotation
cd gradusnotation
npm install
npm run buildTo smoke-test against the production API:
node test-client.mjsOpen an issue at https://github.com/delmas41/gradusnotation/issues. Contributions welcome — small, focused PRs preferred.
MIT — Sean Johnson, Gradus School of Music Composition. See LICENSE.
Search the Gradus music-theory knowledge base for authoritative source material. The corpus includes hand-authored curriculum prose, Bach chorale analysis (408 chorales), score commentaries on 50+ orchestral works, and primary historical sources from Fux (1725) through Boulanger.
WHEN TO USE: before generating notation if you need to look up a specific theory fact — typical voice leading for a Neapolitan-to-V resolution, idiomatic figured-bass realizations of a particular cadence, what makes a chromatic mediant feel like one composer's style versus another. Hitting this first prevents the agent from inventing chord progressions that are stylistically wrong.
WHEN NOT TO USE: for generic music vocabulary ("what is a chord?") that any LLM already knows; for non-theory queries like composer biographies, performance recommendations, or history dates — those are out of scope; for fetching actual score notation (use notation_render or notation_examples instead).
INPUT: provide EITHER topics (kebab-case tags) OR step (curriculum step 1-49). Topics are stronger; step is the fallback when you do not know the canonical topic tag. Both empty returns a MISSING_QUERY error.
OUTPUT (JSON): { ok: true, requestId, chunks: [{ id, sourceType, sourceId, title, content, composer?, era?, topics: string[], curriculumSteps: number[], tokenEstimate }], meta: { query, returnedCount, totalTokens, responseTimeMs }, attribution }. sourceType is one of: kg_concept, score_analysis, score_commentary, bach_chorale_analysis, composer, dictionary, curriculum, lesson_content, practicum, voice_leading, fugue, chorale_exercise, etc. Empty chunks: [] when nothing matched the topics — agent should fall back to its own knowledge or try a different topic tag.
EXAMPLE INPUT: { "topics": ["voice-leading", "deceptive-cadence"], "limit": 3 } TYPICAL LATENCY: 200-700 ms (one Voyage 3 embedding call + Supabase pgvector RPC).
| Name | Required | Description | Default |
|---|---|---|---|
| topics | No | Topic tags in kebab-case. Matched semantically via Voyage 3 Large embeddings plus a topic-overlap boost; exact-match is not required, so close synonyms work. Examples: ["voice-leading","deceptive-cadence"], ["chromatic-mediants"], ["sonata-form","second-theme"], ["figured-bass","6-4-2-chord"], ["fugue","stretto"], ["modulation","pivot-chord"]. | |
| step | No | Curriculum step number (1-49). Fallback when you do not know the topic tag. Maps to the Gradus 10-stage curriculum: Stage I 1-7 (single voice, intervals, scales), II 8-13 (counterpoint, all 5 species), III 14-16 (harmony, third voice), IV 17-18 (form, modulation), V 19-20 (fugue), VI 21-25 (classical style, sonata), VII 26-30 (Romantic harmony, augmented sixths), VIII 31-33 (Impressionist), IX 34-36 (20th century), X 37-40 (advanced). | |
| limit | No | Maximum chunks to return. Default 8 is right for most queries; raise for broad surveys, lower for tight context budgets. | |
| maxTokens | No | Token budget for the combined chunk content. Default 1500 fits comfortably in most agent context windows. The endpoint greedy-selects highest-similarity chunks within this budget. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite lacking annotations, the description thoroughly discloses behavioral traits: output JSON structure, sourceType enums, error on empty inputs, empty chunk behavior, and typical latency (200-700 ms). It covers all necessary operational aspects.
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?
The description is moderately long but well-structured with labeled sections (WHEN TO USE, WHEN NOT TO USE, INPUT, OUTPUT, EXAMPLE INPUT, TYPICAL LATENCY). Every sentence adds value, and the purpose is front-loaded. No wasted words.
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?
Given the absence of an output schema, the description fully explains the output format, including chunk objects with fields, sourceType list, empty chunks behavior, and attribution. It leaves no critical gaps for a search tool.
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?
Schema coverage is 100%, but the description adds significant context beyond the schema. It explains topics as kebab-case tags with semantic matching via Voyage, step as a fallback, and provides usage guidance for limit and maxTokens defaults (8 and 1500) with rationale.
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 clearly states that the tool searches the Gradus music-theory knowledge base for authoritative source material, listing specific corpus contents. It also distinguishes itself from sibling tools by directly referencing notation_render and notation_examples for score notation, making its purpose unambiguous.
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?
The description explicitly provides 'WHEN TO USE' and 'WHEN NOT TO USE' sections, detailing scenarios such as looking up theory facts before generating notation, and excluding generic music vocabulary, non-theory queries, and score notation. It also suggests alternative tools for out-of-scope tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch canonical example inputs (single melody, two-voice counterpoint, chord progression, mixed rhythms with dynamics, string quartet snippet, tied notes across bar lines). Cache the result client-side; the response shape is stable.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It discloses that the response should be cached client-side and that the shape is stable, which is valuable behavioral context for an agent.
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 concise sentences with front-loaded content. The first sentence lists examples clearly, and the second adds caching and stability info. No redundant text.
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?
Given no parameters or output schema, the description is sufficiently complete. It tells what the tool fetches and describes response characteristics, covering all necessary information for a simple fetch operation.
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?
With zero parameters, the baseline is 4. The description adds meaning by enumerating example categories, going beyond the empty schema.
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 clearly states the tool fetches canonical example inputs and lists specific examples like single melody and chord progression. It distinguishes from siblings such as knowledge_search, notation_render, notation_schema, and notation_validate by focusing on examples.
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?
Usage is implied by listing examples, but the description lacks explicit guidance on when to use this tool versus other notation tools. No exclusions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Render music notation from a JSON score. Returns inline SVG, MusicXML, and MIDI in one call. Use scientific pitches ("C4", "F#5", "Bb3") and duration codes (w h q 8 16 32 64 with optional dots). Bar lines are inferred from the time signature; notes that cross bar lines are split and tied automatically. Call notation_validate first if you are unsure your input is well-formed — validate is cheaper than render.
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | Optional title rendered above the score. | |
| composer | No | ||
| tempo | No | ||
| timeSignature | No | ||
| keySignature | No | e.g. "C major", "G minor", "F# major". | C major |
| instruments | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden of behavioral disclosure. It explains that bar lines are inferred from time signature and notes crossing bar lines are split and tied automatically. It also describes the pitch and duration format expected.
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?
The description is a single paragraph that efficiently conveys purpose, output, input formats, behavior, and usage advice. It is front-loaded with the main action and each sentence adds value, though it could be slightly more concise.
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?
Given the tool's complexity and the lack of an output schema, the description provides good coverage of input formats and behavior. However, it does not explain all parameters (e.g., title, composer, tempo) in detail, leaving minor gaps.
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?
Schema coverage is only 33%, but the description adds significant meaning: it explains scientific pitch notation ('C4', 'F#5'), duration codes (w, h, q, etc.), and the structure of notes (shortcut strings vs. objects). However, parameters like title, composer, and tempo are not elaborated beyond the schema.
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 clearly states the tool's purpose: 'Render music notation from a JSON score.' It specifies the output formats (SVG, MusicXML, MIDI) and distinguishes itself from sibling tools like notation_validate by advising to validate first.
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?
The description explicitly tells users when to use notation_validate instead ('if you are unsure your input is well-formed — validate is cheaper than render'). It also explains that bar lines are inferred and notes are automatically split, providing clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch the JSON Schema for the notation_render input shape. Cache the result client-side; this is stable across the v1 API.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries full burden. Discloses stable API result and suggests client-side caching, adding value. No contradictions.
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 sentences, no wasted words. Front-loaded with main action. Every sentence earns its place.
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?
Adequate for a zero-parameter tool. Describes purpose and behavior. Could mention return format, but not essential given simplicity.
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?
No parameters, so baseline is 4. Description adds no parameter info, but none needed.
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 clearly states it fetches the JSON Schema for notation_render input shape, specifying verb and resource. It distinguishes from siblings like notation_render (rendering) and notation_validate (validation).
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?
Implies usage context (fetch schema for notation_render) and advises caching due to stability. Does not explicitly exclude alternatives but given sibling tools, purpose is well-defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Pre-flight validate an input shape without rendering. Returns errors with concrete fix suggestions when input is malformed. Cheaper than notation_render — use this when iterating on input shape.
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | ||
| composer | No | ||
| tempo | No | ||
| timeSignature | No | ||
| keySignature | No | ||
| instruments | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden of disclosing behavior. It mentions it returns errors with fix suggestions and is cheaper, but does not explicitly state that the tool is read-only, idempotent, or free of side effects—common expectations for a validation tool but not confirmed. More explicit behavioral context would be beneficial.
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?
The description is two concise sentences. The first sentence states purpose and output; the second gives usage guidance. No repetition or filler. Essential information is front-loaded.
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?
Given the absence of annotations and output schema, the description covers purpose and usage but omits detail on error types, fix suggestion format, input limitations, or edge cases. It provides a minimal but functional level of completeness, with room for more context.
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 input schema has 6 parameters with 0% description coverage; the description adds no parameter-specific meaning. While parameter names (title, composer, tempo, etc.) are self-explanatory, the description fails to clarify constraints, relationships, or how parameters influence validation. This is a significant gap.
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 explicitly states the tool validates an input shape without rendering, distinguishing it from the sibling notation_render. It uses specific verbs ('validate') and identifies the resource ('input shape'), making the purpose unmistakable.
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?
The description provides clear guidance: 'Cheaper than notation_render — use this when iterating on input shape.' It tells the agent when to use (during iteration) and implies an alternative (notation_render for actual rendering).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
knowledge_search4 fields changedInput schema / properties / limit / descriptionAdded value: +"Maximum chunks to return. Default 8 is right for most queries; raise for broad surveys, lower for tight context budgets."Input schema / properties / maxTokens / descriptionAdded value: +"Token budget for the combined chunk content. Default 1500 fits comfortably in most agent context windows. The endpoint greedy-selects highest-similarity chunks within this budget."Input schema / properties / step / descriptionPrevious value: -"Curriculum step number (1-49) as a fallback if you do not know the topic tag."New value: +"Curriculum step number (1-49). Fallback when you do not know the topic tag. Maps to the Gradus 10-stage curriculum: Stage I 1-7 (single voice, intervals, scales), II 8-13 (counterpoint, all 5 species), III 14-16 (harmony, third voice), IV 17-18 (form, modulation), V 19-20 (fugue), VI 21-25 (classical style, sonata), VII 26-30 (Romantic harmony, augmented sixths), VIII 31-33 (Impressionist), IX 34-36 (20th century), X 37-40 (advanced)."Input schema / properties / topics / descriptionPrevious value: -"Topic tags in kebab-case. Examples: [\"voice-leading\",\"deceptive-cadence\"], [\"chromatic-mediants\"], [\"sonata-form\",\"second-theme\"]."New value: +"Topic tags in kebab-case. Matched semantically via Voyage 3 Large embeddings plus a topic-overlap boost; exact-match is not required, so close synonyms work. Examples: [\"voice-leading\",\"deceptive-cadence\"], [\"chromatic-mediants\"], [\"sonata-form\",\"second-theme\"], [\"figured-bass\",\"6-4-2-chord\"], [\"fugue\",\"stretto\"], [\"modulation\",\"pivot-chord\"]."v0.1.1knowledge_searchnotation_examplesnotation_rendernotation_schemanotation_validateEach tool has a clearly distinct purpose: knowledge_search for theory facts, notation_examples for example inputs, notation_render for rendering, notation_schema for schema retrieval, and notation_validate for input validation. There is no functional overlap.
Tools use a mix of noun_verb (knowledge_search, notation_render, notation_validate) and noun_noun (notation_examples, notation_schema) patterns. Additionally, one tool deviates from the 'notation_' prefix ('knowledge_search'), reducing consistency.
With 5 tools, the server is reasonably scoped for its purpose of music notation rendering and theory knowledge retrieval. It covers core functionality without being overly minimal or excessive.
The tool set covers search, retrieval of examples, input validation, schema access, and rendering. Minor potential gaps (e.g., no tool to list available examples or manage rendered outputs) are not critical for the stated domain.
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Render, validate, encode/decode PlantUML diagram-as-code; 22 diagram types. Free, no auth.
Render video and run AI media tasks from a single declarative JSON request.
Deterministic music theory for agents: analyze, voice, reharmonize, conduct — computed, not guessed
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