Turn messy text into strict JSON schemas agents can trust (invoice, receipt, contact, resume).
Extract HTML or markdown tables into JSON rows.
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | HTML or table content |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It states the core transformation but does not disclose how malformed or partial HTML/markdown is handled, whether multiple tables are supported, how rows are structured, or whether the operation is purely non-destructive.
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?
One concise, front-loaded sentence communicates the essential operation and output. There is no filler or 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?
For a one-parameter tool with no output schema, the description gives a minimally viable contract: input format and output concept. However, it lacks detail on result shape (e.g., array of row objects), error behavior, and handling of edge cases, so it is adequate but not complete.
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 description coverage is 100%: the single 'html' parameter is described as 'HTML or table content'. The description adds 'markdown tables' which is a useful clarification, but it does not meaningfully extend the schema's parameter semantics. Baseline 3 is appropriate.
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 uses a specific verb ('extract'), identifies the resource ('HTML or markdown tables'), and states the output ('JSON rows'). This clearly distinguishes it from sibling tools like parse_invoice or normalize_data without requiring schema inspection.
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 intended use is implied: call this when you have HTML or markdown tables to convert to JSON rows. However, there is no explicit guidance about when not to use it, no alternative sibling routing, and no mention of prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Discover schema shapes. Pass name for one schema, or omit to list all curated schemas.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Schema name; omit to list all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that omitting the name returns all curated schemas and passing a name returns one, which is useful behavioral context. However, it doesn't describe the return format, whether the operation is read-only, error behavior, or what 'curated' means, leaving some gaps for a full understanding.
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 with no filler. The primary purpose is front-loaded, and the optional-name behavior is stated efficiently. Every word 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?
Given the simple one-parameter schema and read-only discovery purpose, the description provides enough for an agent to invoke the tool correctly. However, without an output schema and with no annotations, the lack of detail about the returned schema structure or response format leaves some ambiguity for agents expecting specific return semantics.
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 description coverage is 100%, so the schema already documents the single optional 'name' parameter. The description repeats this behavior without adding new semantic detail, which matches the baseline of 3 for fully-covered parameters.
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 states a specific verb ('Discover') and resource ('schema shapes'), and clarifies the two modes of operation. It doesn't explicitly differentiate from siblings, but the sibling names (extract, normalize, parse, structure, validate) are clearly distinct operations, so an agent can infer that this tool is for retrieving schema definitions.
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 explains how to use the tool (pass a name or omit), but gives no guidance on when this tool should be preferred over siblings or when alternatives might be more appropriate. There is no mention of exclusions, prerequisites, or related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Normalize data object to a named schema (invoice, receipt, contact, resume).
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Input data object | |
| target_schema | Yes | Target schema name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of explaining behavior, but it only restates the operation name. It does not disclose whether the input is mutated, what the return value looks like, how unknown schema names are handled, or whether normalization involves validation or transformation. The schema name examples hint at targets but not at semantics.
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 15-word sentence that front-loads the verb and object and includes the most useful differentiators. No redundant or filler language is present, so it is easy for an agent to scan and retain.
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?
The tool lacks an output schema and annotations, so the description alone must cover return values and behavior. It does not state what the normalized output looks like, whether the four schema names are exhaustive, or what happens on invalid input. For a transformation tool operating on nested objects, this is insufficient for an agent to invoke it confidently.
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 documents both parameters with 100% coverage, so the baseline is 3. The description adds meaningful parameter semantics by enumerating concrete target_schema values (invoice, receipt, contact, resume), effectively providing an informal enum that helps an agent pick valid inputs. It does not add anything about the 'data' parameter beyond what the schema states.
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 verb ('Normalize') and the resource ('data object') with a concrete target: a named schema. The parenthetical examples (invoice, receipt, contact, resume) sharpen the meaning and suggest a distinction from siblings like extract_table or parse_invoice, though the distinction is not explicitly drawn.
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 offers no guidance on when to use this tool instead of sibling tools such as parse_invoice, structure_text, or validate_structure. There is no mention of prerequisites, input types, or scenarios where normalization is preferable to parsing or validation. The use case is only weakly implied by the schema examples.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Parse invoice text into vendor, amount, due_date, line_items (with math_check).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Invoice text or PDF extract |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral burden. It does disclose the output fields and the presence of a 'math_check' on line_items, which gives some behavior beyond a tautology. Still, it does not explain error handling, edge cases, whether the input is considered PDF versus raw text, or any side effects.
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 one compact sentence that is front-loaded with the core action and immediately lists the output fields. There is no filler, repetition, or unnecessary detail.
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?
For a simple one-parameter, no-output-schema tool, the description covers the core action, the input type, and the expected output keys. It is slightly incomplete because the meaning of 'math_check' is not explained, but an agent can still invoke the tool correctly with reasonable confidence.
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 description coverage is 100%: the only parameter 'text' is already described as 'Invoice text or PDF extract.' The description adds no new parameter-level detail, so the schema does the heavy lifting and the description provides no extra compensation.
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 uses a specific verb ('parse') and a specific resource ('invoice text') and lists the target fields (vendor, amount, due_date, line_items). It is clearly aligned with invoice parsing and distinct enough from generic siblings like extract_table or structure_text, though it does not explicitly name or contrast the alternatives.
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 implies the tool should be used when an agent has invoice text or PDF extracts to parse, and the parameter description reinforces this. However, it gives no explicit guidance about when not to use it, prerequisites, or which sibling tools might be more appropriate for different tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert messy text to strict JSON schema. Use for emails, chat logs, unstructured input. Schemas: invoice, receipt, contact, resume.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Raw text to structure | |
| schema_name | No | Target schema: invoice, receipt, contact, resume | generic |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does convey the core transformation behavior and the 'strict' schema constraint, which indicates validation-oriented parsing. However, it does not explain what happens on invalid or ambiguous input, whether the result is a single JSON object, or whether any side effects occur.
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 three short sentences with no filler. It front-loads the core action, then gives usage context, then lists valid schemas. Every sentence contributes distinct information.
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?
For a two-parameter tool with full schema coverage, the description is close to complete: it states purpose, usage context, and supported schemas. However, there is no output schema and no annotations, so the description should more explicitly describe the return value or failure behavior. The phrase 'strict JSON schema' implies the output but does not fully specify it.
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 description coverage is 100%, so the schema already documents both parameters clearly. The description adds a useful list of supported schema names, but these are also repeated in the schema_name parameter description. Therefore the description adds minimal value beyond the structured 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 action ('Convert messy text to strict JSON schema') and names concrete use cases: emails, chat logs, unstructured input. It also lists the supported schemas (invoice, receipt, contact, resume), which makes the resource and scope understandable. However, it does not explicitly distinguish itself from the sibling parse_invoice, which could also handle invoices.
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 gives clear context on when to use the tool: for emails, chat logs, and unstructured input. It does not mention when not to use it or name alternative sibling tools, so it stops short of full routing guidance. The presence of parse_invoice as a sibling makes this exclusion more valuable than it would otherwise be.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate JSON against a schema. Enforces field presence and types (string/number/date/array).
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| schema_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states what is enforced (field presence/types), but does not describe the return value, error behavior, whether validation is read-only, or what happens when validation fails.
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 no filler. The purpose is front-loaded, and every sentence adds relevant information.
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?
For a validation tool with no annotations and no output schema, the description is too thin. It omits return behavior, error handling, available schema names, and practical invocation details needed to call the tool correctly.
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 0%, and the description adds only minimal context for parameters. It implies 'data' is JSON and 'schema_name' identifies a schema, but does not define what a schema_name looks like, how schemas are referenced, or what data structure is expected.
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 names a specific action (Validate), resource (JSON against a schema), and scope (field presence and types). It doesn't explicitly distinguish itself from siblings, but the validation focus separates it clearly from parsing, normalization, and extraction tools.
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?
No guidance is given on when to use this tool versus alternatives like get_schema, normalize_data, or parse_invoice. The agent must infer usage entirely from the tool name and general semantics.
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.
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Agent-native document parsing: PDF, scans and FR/EU invoices to structured JSON or Markdown.
Turn any PDF into structured JSON via AI + OCR: invoices, bank statements, contracts.
Deterministic JSON repair, validate, example-gen, schema-coerce for agents. Zero LLM, sub-10ms.
Turn PDFs, scans and photos into a queryable database. Invoices, CVs, receipts, in bulk.
Add one secure layer between your agents and this server.
There is meaningful overlap between parse_invoice, structure_text, and normalize_data, especially since structure_text and normalize_data both target the same schemas and parse_invoice is a more specific version of structure_text for invoices. extract_table, get_schema, and validate_structure are distinct, but the unstructured-text-to-schema tools have unclear boundaries.
All tool names follow a consistent verb_noun snake_case pattern: extract_table, get_schema, normalize_data, parse_invoice, structure_text, validate_structure. This makes the tool set predictable and easy to navigate.
Six tools is a well-scoped size for a data structuring and validation server. Each tool addresses a meaningful step in the pipeline without excessive redundancy or unnecessary surface area.
The core workflow of extracting, normalizing, and validating structured data is covered, with schema discovery available. The main gap is the lack of a way to create or modify custom schemas, but the curated-schema approach makes this a minor limitation rather than a critical failure.