This server compiles and explains LLM-ready context for code changes in a repository, plus verifies retrieval quality from git history.
compile_context — given changed symbols or a git ref (and optionally a task description), pack the relevant callers/callees/related functions into a token-budgeted context, with a disclosure header of dropped items.
find_impact — get the blast radius of changing a symbol: direct callers, callees, and transitive impact.
explain_selection — see which symbols were included or dropped, with scores and token costs, so an agent can inspect or filter the selection.
verify_retrieval — mine git history to generate co-change test cases, run retrieval against them, and report hit/recall; prints NULL RESULT when the tool doesn't fit the repo.
Show an AI coding assistant only the code that matters for the change it is making.
DiffContext is a context compiler for LLM coding agents. Give it a Python repository and a change — a git diff, a branch, or a single function name — and it returns the small set of functions the model actually needs to make that change safely: the callers that will break, the subclasses that override it, the tests that cover it. It fits them to whatever token budget you have, and it tells the model what it had to leave out.
It is built for people wiring LLMs into real codebases — agent loops, PR review bots, CI checks — anywhere you have to decide what goes in the prompt and the repository is far too large to send.
And it grades itself: point it at your repo and it mines your git history, runs retrieval against real co-change pairs, and prints NULL RESULT when it doesn't fit — finding that out is the feature.
Ask an assistant to change one function in a 50,000-line project and you have
three bad options: paste the whole repository (it does not fit, and models get
worse in very large contexts), paste just that one function (the model breaks
three callers it never saw), or grep for the name (grep cannot find the
subclass that overrides it, or the handler that receives it through
functools.partial — we measured grep's recall plateauing no matter how
much budget you give it).
DiffContext is the fourth option. Parse the repository once into a real dependency graph, then for any change select the few functions that actually matter and pack them into the smallest useful prompt.
git change ──► changed functions ──► hybrid retrieval ──► token budget ──► LLM-ready context
graph ∪ BM25 ∪ file top-k + tokensRelated MCP server: Serena
pip install diffcontextZero runtime dependencies, Python 3.9+.
For MCP integration (Claude Code / Cursor / Windsurf):
pip install "diffcontext[mcp]"See docs/MCP.md for the server config.
From source for development:
git clone https://github.com/trakshan-mishra/Diffcontext.git
cd Diffcontext && pip install -e .diffcontext index /path/to/project # cold: seconds; warm: ~0.02s
diffcontext compile --ref HEAD~1 --max-tokens 8000
diffcontext verify --from-history 20 --calibrateMore commands: USAGE.md. Production recipes: docs/USE_CASES.md.
diffcontext verify --from-history 20 --calibrate mines test cases from
your repo's git history and grades retrieval against them — and prints
NULL RESULT rather than a decorative number when the tool doesn't fit
your repo. Finding that out is the feature.
Yes — measured end to end, not by proxy. On 128 ContextBench Python tasks judged by each repository's own test suite (no LLM-as-judge), context roughly quadruples pass@1: 5.5% → 25.8%, exact McNemar p < 0.0001.
Two qualifiers, both in benchmarks/contextbench/RESULTS.md
§6: (a) the seed functions given to every arm are oracle — extracted
from the gold patch — so this measures "given correct localization, does
context quality matter?", not end-to-end issue solving (localization is
handed to every arm for free); (b) 121 of the 128 effective tasks are
django, so this is largely a django result.
The honest companion: the three context variants (default / gap / depboost)
are statistically indistinguishable from each other, p = 0.36–0.81. The
win is context versus no context — not this selector versus that one. Full
results: benchmarks/contextbench/RESULTS.md.
Not a code generator. It selects and packs context; the model writes the code.
Not precision-first. It casts a wide net — mean precision is under 0.1
at the default top-k. Use --cutoff gap if you pay per token.
Not multi-language yet. Python is fully supported. TypeScript/JS (ESM) is a working prototype; CommonJS is a measured failure mode.
Not a replacement for reading the code. Static analysis has blind spots, itemized below and in docs/BENCHMARKS.md.
Ground truth is mined from git history — a developer changed these functions together in one commit; shown one, does the tool find the others? Measured on 701 real commits across 9 Python repositories, and re-run as a CI gate on every push so quality cannot silently regress.
Per-commit hit / recall of real co-change partners, hybrid retrieval:
django | click | flask | httpx | pydantic | black* | requests* | |
Hit | 0.894 | 0.889 | 0.863 | 0.935 | 0.758 | 0.897 | 0.953 |
Recall | 0.774 | 0.750 | 0.694 | 0.772 | 0.536 | 0.712 | 0.762 |
* validation repos, never used for tuning. Full table across all 9 repos: benchmarks/README.md.
Head-to-head vs grep at identical token budgets, grep plateaus at
0.215 recall past 4k tokens while DiffContext reaches 0.576 at 8k
(2.7×). The honest flip side: mean precision is under 0.1 at the default
top-k — most retrieved symbols are supporting context, not the exact
co-change set. --cutoff gap cuts at the largest score drop for ~4×
precision at ~30% recall cost (co-change benchmark; 2.2× / ~14% on
ContextBench).
A 2026-07 pass attacked the evaluation instead of the tool. Three published numbers did not survive:
Calibration — the only citable number (r=0.274, n≈25) was measured on a polluted index. Re-measured clean at n=1,080 the legacy score gets r=0.016 (p=0.60): no relationship at all. Fixed by shrinking toward "don't know" → r=0.287 (p=0.0001) — a ranking signal, not a probability.
Blend weights — the shipped [0.5, 0.35, 0.15] failed leave-one-repo-out; every fold picked a less graph-heavy blend. Now [0.3, 0.5, 0.2].
Dense baseline — a TF-IDF stand-in had overstated dense retrieval (0.664, beating BM25 5/5). The real MiniLM encoder scores 0.597 and beats BM25 only 2/5. Two prior conclusions corrected on the record.
Full write-up: docs/auditing-my-own-benchmark.md · raw pass: benchmarks/RIGOR_REPORT_2026-07.md.
from diffcontext.pipeline import index_repository, analyze_impact, compile
idx = index_repository("/path/to/repo")
impact = analyze_impact(idx, ["./src/auth.py:validate_jwt"])
ctx = compile(idx, impact, max_tokens=8000, top_k=20)
print(ctx.text) # paste-ready, meta-header discloses what was droppedIncremental API (idx.update([...])), structured output, pluggable tokenizer:
docs/ARCHITECTURE.md.
Language | Status | Retrieval quality |
Python | Full | Benchmarked: 701 commits, 5 repos + 4 validation repos |
TypeScript / JS (ESM) | Prototype | Mean recall 0–68% depending on code style |
JavaScript (CommonJS) | Unsupported | Measured 0.0% on express — do not use |
Static analysis has a ceiling: thematic siblings with no call between them,
cross-subsystem conceptual links (all methods score 0/20), and dynamic
dispatch are measured blind spots — itemized in
docs/BENCHMARKS.md. When in doubt:
grep -rn "function_name(" --include="*.py" . before fully trusting
"no callers found."
docs/ARCHITECTURE.md — pipeline, module map, agent API
docs/BENCHMARKS.md — all numbers, downstream pass@1, limitations
docs/MCP.md — MCP server for Claude Code / Cursor / Windsurf
docs/ROADMAP.md — prioritized plan with measured motivations
diffcontext-service/ — FastAPI service + web UI
observability/ — retrieval pipeline tracing
CONTRIBUTING.md — setup, CI gates, adapter development
MIT
Compile LLM-ready context for a change.
Give it changed symbol IDs (e.g. ./src/auth.py:validate_jwt) or a git ref (e.g. HEAD~1), and it returns the callers, callees, and related functions the model needs to make the change safely — packed into max_tokens with a disclosure header showing what was dropped.
Optionally pass task_description (the bug report or issue text) to bias retrieval toward symbols relevant to the described problem — the one signal the graph alone can't provide.
Args: repo_path: Absolute path to the repository. If omitted, uses the --repo from server startup. changed_symbols: List of changed symbol IDs (e.g. ["./src/auth.py:validate_jwt"]). Mutually exclusive with git_ref. git_ref: Git ref to detect changes from (e.g. "HEAD~1"). Mutually exclusive with changed_symbols. When only task_description is given (no changed_symbols or git_ref), defaults to "HEAD". task_description: The bug report or issue text. Biases retrieval toward symbols semantically related to the described problem, not just structurally near the changed symbols. max_tokens: Token budget for the context (default 8000). meta: Disclosure header level: "full" (default), "compact", or "off". The pass@1 effect of meta level is UNMEASURED.
| Name | Required | Description | Default |
|---|---|---|---|
| meta | No | full | |
| git_ref | No | ||
| repo_path | No | ||
| max_tokens | No | ||
| changed_symbols | No | ||
| task_description | No |
| Name | Required | Description |
|---|---|---|
| result | 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 exposes useful behavioral details: output is 'packed into max_tokens', a disclosure headers shows what was dropped, retrieval can be biased by task_description, and the pass@1 effect of the meta level is explicitly marked 'UNMEASURED'. These are substantive disclosures, not merely rephrased schema information.
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 front-loaded with the core action and then expands into concrete input formats, defaults, and caveats. It contains no filler, yet it is rather long due to the detailed Args block. Every sentence earns its place, and the most critical behavioral caveat ('UNMEASURED') is included without unnecessary qualification.
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 6-parameter tool with no annotations and no input-schema descriptions, the description covers all required behavior: input formats, defaults, output composition, token budget, disclosure header, and task bias. The one gap is explicit behavior when all inputs are omitted: it only explains defaulting to HEAD when 'task_description' is given, not what happens when no parameters at all are passed. This is a small but real completeness gap.
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?
Although input-schema content coverage is 0%, the description's Args block thoroughly documents all six parameters with examples, defaults, mutual exclusivity, and behavioral caveats. It explains the exact meaning of changed_symbols and git_ref, the default git_ref when only task_description is passed, and the enum-like behavior of meta. This fully compensates for the sparse 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 opening sentence states a specific verb and resource: 'Compile LLM-ready context for a change.' It further clarifies the resource by naming the accepted inputs (changed symbol IDs or git ref) and the returned content (callers, callees, related functions). It does not explicitly differentiate from the sibling tools, but the purpose is clear enough to be usable in isolation.
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 strong input-selection guidance: changed_symbols vs git_ref are mutually exclusive, and task_description alone implies HEAD. However, it says nothing about when to use compile_context over its siblings (find_impact, explain_selection, verify_retrieval), so the when-to-use guidance is limited to parameter choices rather than tool-disambiguation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Explain why symbols were included or dropped from context.
Returns the included symbols (with scores and token costs) and the dropped symbols (scored but cut by the token budget), so an agent can inspect or filter the selection.
Args: repo_path: Absolute path to the repository. If omitted, uses the --repo from server startup. symbol: The changed symbol ID to build context for. max_tokens: Token budget (default 8000).
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | ||
| repo_path | No | ||
| max_tokens | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden here. It is transparent about the behavior: included symbols are returned with scores and token costs, and dropped symbols are scored but cut by the token budget. It does not explicitly state side effects or authorization requirements, but its read-only nature is strongly implied.
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 front-loaded with the core purpose, then the return behavior, then a compact Args block. Every sentence adds value; there is no filler or unrelated 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?
Despite having no annotations, the description covers the essential aspects needed to invoke the tool: what it does, what it returns, and what each parameter means. The only notable gap is the lack of comparative routing guidance against the sibling tools.
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 property descriptions have 0% coverage, so the description must compensate. It does: repo_path is 'Absolute path to the repository' with a fallback to the --repo startup value, symbol is described as 'The changed symbol ID to build context for,' and max_tokens is defined as a 'Token budget' with a default. This adds meaningful semantics beyond the raw 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 with a specific verb and resource: 'Explain why symbols were included or dropped from context.' It also details what is returned. It does not explicitly compare itself to siblings such as compile_context or verify_retrieval, but the purpose is specific enough to be distinguished from them.
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 usage context: after context selection, an agent can 'inspect or filter the selection.' It also explains parameter defaults such as using the startup repo when repo_path is omitted. However, it does not explicitly state when to use this tool over its siblings or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find what breaks if you change a symbol.
Returns the blast radius: direct callers, direct callees, and transitive impact. The "what breaks if I change this" query.
Args: repo_path: Absolute path to the repository. If omitted, uses the --repo from server startup. symbol: Symbol ID (e.g. ./src/auth.py:validate_jwt).
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | ||
| repo_path | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral burden. It clearly discloses the output: direct callers, direct callees, transitive impact, and even describes behavior when repo_path is omitted, using the server startup --repo. It does not explicitly call itself read-only, but the language 'Find... Returns' strongly implies a query-only operation.
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 short, front-loaded with the core purpose, and each sentence earns its place. The summary 'The "what breaks if I change this" query' is a useful clarifying mental model rather than unnecessary repetition. The two argument descriptions are compact and relevant.
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 fairly simple 2-parameter lookup tool, the description covers the inputs, the core behavior, and the scope of results, and an output schema exists to fill in any return-format uncertainty. It doesn't go quite further to explicitly mention read-only constraints, staleness, or required indices, but there are no annotations and the description is still sufficiently rich for most AI agents.
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?
Input schema has 0% description coverage, but the description fully compensates for both parameters: repo_path is defined as an absolute path with the startup --repo fallback, and symbol is given an exact Symbol ID example (./src/auth.py:validate_jwt). This adds significant, actionable detail beyond the raw 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: finding what breaks if a symbol is changed, and what it returns: direct callers, direct callees, and transitive impact. It is specific enough to be distinguished from generic helpers, but it does not explicitly differentiate from sibling tools like compile_context or verify_retrieval.
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 phrase "The 'what breaks if I change this' query" gives a strong, useful usage frame: call this when you are about to change a symbol and want to assess impact. However, there is no explicit statement about when not to use it, no comparison to alternatives, and no exclusions, so the guidance remains implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Mine git history and grade retrieval quality on your repo.
Generates test cases from co-change history, runs DiffContext retrieval against them, and reports hit/recall. Prints NULL RESULT when the tool doesn't fit your repo — finding that out IS the feature.
Args: repo_path: Absolute path to the repository. If omitted, uses the --repo from server startup. n: Maximum number of test cases to generate from git history (default 20).
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | ||
| repo_path | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even though no annotations were provided, the description reveals significant behavioral traits: it generates test cases from co-change history, runs DiffContext retrieval, reports hit/recall, and optionally prints NULL RESULT when the repo doesn't fit. Calling out the NULL RESULT as a feature is genuinely useful and goes beyond typical descriptions.
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 definition is short, front-loaded with the main purpose, then narrows to mechanism and parameters. The NULL RESULT sentence is pointed and earns its place. The Args section provides compact parameter context without re-describing what the schema already defaults.
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 that there is an output schema and only two simple parameters, the description is complete: it covers what the tool does, the algorithm, output metrics, edge-case behavior, and all parameter semantics. An agent has enough information to select and invoke this 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 description coverage is 0%, so the description carries all param documentation. It clearly adds meaning by explaining repo_path as an absolute path with a startup fallback to --repo, and describes n as the maximum number of test cases with a default of 20. This covers both parameters meaningfully.
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 opens with a specific verb and resource: 'Mine git history and grade retrieval quality on your repo,' and then defines the mechanism (generate test cases, run retrieval, report hit/recall). It clearly explains what the tool does, but it does not explicitly contrast it with sibling tools like compile_context or find_impact.
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?
It gives clear context: you use this when you want to assess retrieval quality over git history, and the NULL RESULT warning indicates what to expect if the repository isn't a good fit. However, it does not state explicit when-to-use versus known sibling alternatives.
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.
v0.5.4compile_contextexplain_selectionfind_impactverify_retrievalcompile_context and find_impact overlap substantially because both return callers/callees for a symbol or change, requiring an agent to read descriptions carefully to pick the right one. Their intended uses are reasonably distinct—compile_context produces token-budgeted LLM context, while find_impact answers 'what breaks'—and explain_selection and verify_retrieval are clearly separate.
All tool names follow the same snake_case verb_noun pattern: compile_context, find_impact, explain_selection, verify_retrieval. There are no mixed conventions or vague names.
Four tools is an appropriately focused set for this domain: a primary retrieval/context tool, an impact-focused variant, an introspection tool, and an evaluation tool. None of the tools feel redundant or extraneous.
The main workflow is covered: generate context, inspect impacts, explain selection decisions, and verify retrieval quality on the repo. There is a minor gap in that there is no tool to directly explore the raw dependency graph outside these retrieval wrappers, but that is a narrow gap for this tool's stated purpose.
Deterministic context layer for your codebase: change impact, blast radius, answers with receipts.
Project memory, semantic code search, and grounded agent context.
Provide your AI coding tools with token-efficient access to up-to-date technical documentation for…
Codebase intelligence for agents: 152 structured artifacts across 21 programs, one call.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/trakshan-mishra/Diffcontext'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
